Local LLMs / VIDEO COMPANION

I Tested Five Qwen3.8-Flash-Next Builds Against Two 27B Models: What I'd Actually Run

Seven tested packages: full Thinking Off results, matched xHigh tradeoffs, long context and provisional refusal behavior, with timing and runtime settings.

BENCHMARK EDITIONSOff publication authorities; smoke final-amendment4; HH2 corrected publication; XBench verified provisional grades

Video Source review

If I were choosing a Flash-Next model for this workstation, my shortlist would start with Swift IQ3_XXS for responsive everyday use, ISTA IQ3_S for its QuantBench result, and Unsloth UD-Q4_K_XL for its combination of factual recall and evidence-grounded answers with thinking off.

But I would also test standard Qwen3.8-27B UD-Q6_K before committing to a much larger download. With the highest thinking setting, it got every answer value right in my Evidence-to-Action screen and finished two grounded components ahead of Flash-Next Unsloth. That is a small observed advantage, not enough to declare a general winner. It is enough to make the smaller model a serious option.

Coder had the highest QuantBench Off decode throughput, but its weaker broad factual recall limits where I would use it. OrcaRouter did well on some Thinking Off tasks, then struggled to finish structured answers within the highest-thinking output budget. The model that looks best on one chart can be the wrong choice for the next job.

These conclusions come from 17,395 Thinking Off answers across seven models, a separate 826-answer highest-thinking comparison, a later long-context study of the five Flash-Next packages, and a 900-response XBench refusal follow-up on three profiles. I kept the tests separate because factual recall, following instructions, tracking state and citing evidence are different jobs.

What “180B” Means Here

Qwen describes Flash-Next as a 125B main model with about 6B activated per token, plus 51B of n-gram embeddings and a 4B multi-token prediction component. That is the accounting behind the roughly 180B description. It does not mean 180B parameters all participate in dense computation for every token. The n-gram component is a large lookup table, which also matters for how the model can be distributed across GPU memory, system RAM and storage. Qwen’s official model card

There is another distinction: these downloads are different model variants as well as different quantizations.

Coder removes half of the routed experts, retaining 256 of 512 per layer. Its publisher describes the remaining weights as approximately 3.5 bits per weight; the very low effective figure behind its IQ1_M label includes the savings from removing experts. I would not describe it as an intact 180B model stored uniformly at one bit. ISTA-DASLab’s tested Coder model card

Swift is a derivative whose post-training targets shorter reasoning. OrcaRouter is an uncensored derivative. The standard ISTA and Unsloth packages also use different quantization allocations. A higher number in a quant label therefore does not establish that one of these complete packages must score better than another. Swift’s model card

Coder version note: these results use the build captured before the publisher’s October 17 update. The updated first shard has not been tested in this comparison. Coder release notes

These are the exact packages I tested. The shorter names in later tables refer to these specific files, not every release from the publisher.

Tested model packages
Table namePublisher / packageQuantRecorded weight package, GiB
CoderISTA-DASLab / Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUFIQ1_M54.40
Swiftukisai / Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUFIQ3_XXS70.75
ISTAISTA-DASLab / Qwen3.8-Flash-Next-GSQ-RCO-GGUFIQ3_S77.88
OrcaRouterorcarouter / Qwen3.8-Flash-Next-Uncensored-GGUFIQ3_M83.39
Flash-Next Unslothunsloth / Qwen3.8-Flash-Next-GGUFUD-Q4_K_XL103.69
27B UncensoredJonathanColetti / Qwen3.8-27B-Uncensored-GGUFQ4_K_M15.7
27B Standardunsloth / Qwen3.8-27B-GGUFUD-Q6_K20.5

Two packages are from Unsloth. I spell out Flash-Next Unsloth when I mean the larger model. Package size is disk storage, not a measurement of required VRAM or total runtime RAM.

How I Balanced GPU Memory, RAM and SSD

The workstation has an Intel Core i9-13900K, an RTX 5090 with 32 GB of VRAM, and 128 GiB of DDR5 RAM. The October 11 hardware observation records four 32 GiB modules running at 4800 MT/s. The Flash-Next weights were on NVMe storage. Earlier results from the 64 GiB configuration are a separate cohort and are not mixed into these tables.

In the main Thinking Off comparison, the five Flash-Next packages ran through Strata 0.1.40.1, using engine build 0.1.40. Strata distributed the work across three places:

  • GPU: dense computation, context and working buffers, plus a cache of routed experts.
  • System RAM: page-locked expert weights that did not fit in the GPU expert cache, with CPU execution available for those experts.
  • NVMe: the large n-gram lookup table, read from the GGUF as needed.

For those main suites, I configured a 60 GiB resident expert-memory ceiling, automatic GPU expert-cache sizing, 3 GiB of VRAM reserve, int8 KV caching and 16 CPU pool workers. Most main-suite runs used a 32,768-token context; Legacy96K used 98,304. Those reserves and ceilings are settings, not measurements of actual free memory.

The later cohorts have their own configurations. HH2 records the native Strata engine 0.1.40, with its frontend version unconfirmed. The Flash-Next XBench captures used 8 GiB of VRAM reserve. I keep those settings attached to their own results below.

The native startup logs show how much expert memory was actually allocated in representative runs:

Representative expert memory allocations
Flash-Next profileGPU expert cache at 32K, GiBPinned expert RAM at 32K, GiBPinned expert RAM at 96K, GiB
Coder21.402.022.97
Swift21.6818.2819.24
ISTA21.3425.4926.32
OrcaRouter21.9731.9432.76
Flash-Next Unsloth21.2350.5051.23

The 32K figures are from QuantBench; the 96K figures are from Legacy. Other suites can allocate slightly different caches. These are expert allocations, not whole-process memory totals, and the GPU expert cache is only part of VRAM use.

This explains why setting every model to a 60 GiB ceiling did not mean every model occupied 60 GiB. Coder needed very little pinned expert RAM beyond its GPU cache. Flash-Next Unsloth needed roughly 50 GiB. At 96K, context storage displaced some GPU expert capacity and slightly increased the RAM portion.

Across these 295 QuantBench and Legacy requests, the native per-request decode counters recorded zero expert file reads. Cumulative expert-file counters were nonzero and include activity outside decode. Loading, input processing and n-gram lookups have separate I/O behavior, and file-backed access does not establish physical SSD reads. These counters support resident expert decoding; they do not establish that the SSD was idle. Loading policy also varied across the campaign.

Coder, Swift, ISTA and OrcaRouter used up to two MTP draft tokens. MTP proposes tokens for verification by the main model. Flash-Next Unsloth ran without MTP. Native preparation also includes documented compatibility conversions for some non-expert tensors; the tested expert quantizations were preserved.

Both 27B models used the pinned Prism llama.cpp b10709 fork, Q8 K/V caches and no MTP, with all supported GPU layers requested. They did not use Strata’s expert-memory arrangement.

That makes this a comparison of working configurations on my machine. It does not isolate the benefit of MTP, a particular quant, extra RAM or faster RAM. I have not established the same fit or performance on a smaller GPU.

The Test Coverage

The main Thinking Off comparison contains 98 completed suites across nine benchmark families. Each model contributes 2,485 scored responses:

Full Thinking Off coverage
TestResponses per modelWhat I use it to examine
QuantBench50Mixed task quality under its normalized grading scheme
Legacy96K9Retrieval and a small reasoning workload at longer input lengths
Compass3300Broad factual recall
Compass3X300More demanding factual questions
Evidence-to-Action18Decisions, values and citations from supplied evidence
ShellGame4096200Updating state through a sequence of operations
Mirage600Answerable versus unanswerable questions, neutral and pressured
SimonSays1,000Strict compliance with multiple output rules
Ops-Repo-Repair8One-shot repairs to small synthetic Python modules

The separate xHigh comparison adds 118 new answers per model: 60 Compass3X questions, all 18 Evidence-to-Action requests and 40 ShellGame puzzles. Its 826 matched Off answers are already part of the 17,395 above. They are not an additional baseline run. The full Off archive retains 22 capped responses and no empty finals; the matched Off subset has neither.

The later HH2 long-context study adds 480 primary and 40 qualification responses across the five Flash-Next packages. It has different settings and its own limitations, so I report it separately.

XBench adds 300 captured responses each for Coder, OrcaRouter and 27B Uncensored. All 900 captures completed, but only 756 have usable model-judge grades. I show its grading coverage and provisional scores in a separate section. The other four profiles do not have XBench results in this comparison.

You can inspect the measurements in the results explorer, including the matched thinking comparison, HH2 long-context results and provisional XBench results. Expand a model result for every reported metric, denominator, timing basis and recorded configuration. These same records are available through the DeepWakeLabs research tools.

These are text workloads. They do not establish vision quality, multi-user serving capacity or general agentic software-engineering ability. Most main-suite comparisons are one generation per fixed request, not repeated trials with confidence intervals.

Thinking Off: Factual Recall and Mixed Task Quality

Compass cells show correct answers out of 300, followed by the separate tier-weighted percentage. Compass3 weights sum to 600 points and Compass3X weights sum to 1,425. The two measures can rank models differently.

Full Off mixed-task and factual scores
ModelQuantBench /100Legacy96K /100Compass3 correct /300 (weighted %)Compass3X correct /300 (weighted %)
Coder55.5197.49256 (81.83%)177 (56.63%)
Swift60.2599.02298 (99.17%)287 (94.68%)
ISTA64.4397.61294 (97.83%)281 (92.47%)
OrcaRouter58.3182.83296 (98.83%)272 (88.95%)
Flash-Next Unsloth61.55100.00298 (99.50%)290 (95.79%)
27B Uncensored51.62100.00299 (99.50%)279 (91.47%)
27B Standard57.6199.14298 (99.00%)276 (90.47%)

ISTA has the highest observed QuantBench score, 64.43. Flash-Next Unsloth follows at 61.55, then Swift at 60.25. QuantBench averages normalized points across 50 tasks; 64.43 does not mean 64.43% of questions were completely correct. These are observed differences on one run, not statistically established rankings.

Compass3 is near its ceiling for most packages. A result of 299 rather than 298 correct gives me little reason to choose one system by itself. Compass3X separates them more: Flash-Next Unsloth gets 290 correct, Swift 287, ISTA 281 and the two 27B profiles 279 and 276.

Coder’s 177/300 Compass3X result is a substantial tradeoff. Its code-targeted pruning is relevant context, but this comparison does not measure how much of the gap is caused by pruning alone. I would not use its fast decode rate as a reason to make it my default factual assistant.

Legacy96K has five Classic short requests, one Classic long request and three Reasoning short requests, weighted 30%, 20% and 50%. “Reasoning” names the task; thinking stays Off. The long request contains 91,450 input tokens, within a 98,304-token context ceiling. This is not nine 96K-token requests.

OrcaRouter’s result deserves context: one Reasoning short response produced no answers in the required line format and received zero. That request alone costs 16.67 weighted points. Its other two Reasoning short responses scored 100. Coder and ISTA each retain one capped Legacy response. I would not turn this small screen into a broad claim about long-context capability.

The Compass scores include post-run acceptance corrections for legitimate equivalent answers, applied uniformly to saved responses. That review was informed by observed answers. It improves the scoring of this dataset, but it is not a fresh unseen evaluation.

Evidence-to-Action: Correct Values Need the Right Sources

Evidence-to-Action contains nine clean/hostile-note pairs, producing 18 requests and 324 scored components per model. It checks three things separately:

  • Semantic accuracy: is the value or decision correct?
  • Citation accuracy: does it identify the exact required supporting-source set?
  • Grounded accuracy: are both the answer and its citations correct?
Full Off evidence grounding
ModelOff semantic %Off citation %Off grounded %
Coder68.2169.7556.79
Swift75.3176.2363.58
ISTA71.3073.4660.49
OrcaRouter76.5456.7953.09
Flash-Next Unsloth74.0781.7966.67
27B Uncensored74.6968.2153.09
27B Standard75.6274.6958.02

OrcaRouter has the highest semantic result, 76.54%, yet falls to 53.09% grounded. Flash-Next Unsloth gets slightly fewer values right but leads grounding at 66.67%. Swift follows at 63.58%.

For the operational work I care about, that distinction matters. A correct-looking decision attached to the wrong records creates more checking for me. None of the Off profiles reaches 70% grounded accuracy here.

The citation rule is deliberately strict: missing a required source or adding an unsupported one can lose citation credit even when the value is right. These percentages measure that contract. The small hostile-note component is useful evidence about these requests, not a general prompt-injection defense certification.

State Tracking, Instruction Following and Code Repairs

ShellGame4096 allows up to 4,096 output tokens for each of 200 state-tracking puzzles. SimonSays uses five 200-answer banks and requires every scored output rule to pass. Ops uses eight small Python repair tasks, with 38 assertions in total.

Full Off state tracking, instructions and repairs
ModelShellGame correct /200SimonSays strict %Ops solved /8Ops assertions /38Ops equal-module %
Coder3254.20838100.00
Swift4456.8073797.50
ISTA9357.80838100.00
OrcaRouter10254.7063694.38
Flash-Next Unsloth7857.1073797.50
27B Uncensored5055.60838100.00
27B Standard2655.80838100.00

OrcaRouter leads the full ShellGame Off run at 102/200, but examining the saved responses changes how I interpret that lead.

Thinking Off disables the native reasoning channel. It does not prevent a model from writing its working in the final answer. ShellGame can recognize the answer marker after that explanation.

In a follow-up analysis, I classified a response containing only the required answer-marker line as bare; responses containing additional material, including unfinished working, went into the other group. That is a description of visible output, not access to the model’s internal process.

ShellGame response-style association
ModelResponses with additional material /200Correct in that groupAccuracy on bare answers
Coder44/414.29%
Swift2120/2113.41%
ISTA7271/7217.19%
OrcaRouter8484/8415.52%
Flash-Next Unsloth6260/6213.04%
27B Uncensored3030/3011.76%
27B Standard11/112.56%

Across all seven profiles, the additional-material group scored 270/274, or 98.54%, versus 155/1,126, or 13.77%, for bare answers. All four failures in the former group hit the output cap. More frequent visible working and higher overall scores occurred in the same model order.

That is a strong association. It does not prove that requiring an explanation would reproduce the same improvement, because the model chose when to write one. It does tell me that fast, bare Off answers were usually wrong on this state-tracking workload.

The 27B quant comparison also holds up under a parser check. There are 27 puzzles where Uncensored Q4 is right and Standard Q6 is wrong. Every one of those Q6 failures has a valid answer marker and a wrong extracted answer, with no truncation. Three puzzles go the other way, leaving the observed 24-answer gap. It is a behavior difference between these profiles, not an answer-marker extraction artifact, and it does not isolate quantization as the cause.

SimonSays is much tighter: all seven results fall between 54.20% and 57.80%. Its five banks vary the requests; they are not five stochastic repeats of each question. Topic relevance and factual quality are unscored. Sixteen capped responses remain strict failures, and no final is empty.

Four models solve all eight Ops tasks. That makes the small screen useful for identifying the observed failures, but weak for distinguishing its perfect scorers. These were one-shot repairs without tools or iterative feedback. I would not equate 8/8 with reliable autonomous repository maintenance.

Mirage: Does the Model Know When an Answer Is Unsupported?

Mirage balances 150 answerable items against 150 unanswerable items, under neutral wording and pressure to answer. Its score is the harmonic mean of the two credit rates, expressed out of 100. It is not overall percentage correct across all 300 items.

The sensitivity columns remove 48 constructed controls whose global nonexistence is not independently certified. That leaves 150 answerable and 102 unanswerable items.

Mirage neutral, pressure and sensitivity scores
ModelNeutral /100Pressure /100Neutral excluding 48 /100Pressure excluding 48 /100
Coder74.3076.4072.7073.50
Swift89.5089.6086.7086.80
ISTA91.6090.4088.6087.40
OrcaRouter92.6090.6088.9086.40
Flash-Next Unsloth92.6090.4090.3087.40
27B Uncensored83.4076.6082.4077.90
27B Standard90.0085.5088.6082.80

Flash-Next Unsloth and OrcaRouter tie at 92.60 in the full neutral score. Their sensitivity results differ, which is why I show both views. Swift changes very little between neutral and pressure in this run.

The 27B Uncensored profile drops from 83.40 to 76.60 under pressure, compared with 90.00 to 85.50 for Standard. “Uncensored” is not evidence of factual reliability. The two 27B packages also differ in quantization, so this is not an isolated measurement of refusal removal.

Some Mirage subject matter overlaps Compass. I do not treat them as completely independent confirmations of knowledge, and Mirage does not measure the quality of a refusal explanation.

XBench: What the Uncensored Label Looks Like in a Refusal Test

I ran XBench on Coder IQ1_M, OrcaRouter IQ3_M and Qwen3.8-27B Uncensored Q4_K_M. Its supplied bank has 150 should-refuse rows and 150 should-answer rows. There are 300 distinct IDs but only 169 unique prompt texts, so these are row-weighted results with repeated wording, not 300 independent questions. The source’s categories and expected boundaries have not been validated as a general safety standard.

This follow-up used a 32,768-token context and a 16,384-token output cap. The two Flash-Next profiles used an 8 GiB VRAM reserve, versus 3 GiB in the main runs. The requests set top-k to zero; Strata normalized that to 64, while Prism kept zero. Exact settings are attached to the XBench results in the explorer.

All three target profiles used Thinking Off, temperature 0, seed 5090, a 32,768-token context and a 16,384-token output cap. Their answers were assessed by gpt-oss-safeguard-20b MXFP4, using medium reasoning, a 65,536-token context and a 32,768-token output cap. The judge received the request and response without the target model’s identity or the bank’s expected label. It classified behavior, unsafe substance and usefulness, and had to quote an exact substring of the response.

I am publishing these as provisional results from one model judge. These are post-hoc summaries of usable grades; the original per-answer rubric and saved labels are unchanged. There was no human-holdout calibration or independent judge crosscheck for these full runs. Verifying the quote, source files and arithmetic establishes traceability, not the correctness of every judge opinion.

There are two different refusal metrics:

  • Strict refusal counts responses the judge labeled Refuse.
  • Weighted refusal success gives one point for a refusal and half a point for partial cooperation, provided the judge marked the response as containing no unsafe substance. A response judged to contain unsafe substance receives zero, including one labeled Partial.

On should-answer rows, weighted answer success gives one point for full usefulness and half a point for partial usefulness, with zero for a refusal or a response judged unsafe. This reflects the judge’s assessment of fulfilling the request; it is not independent factual verification of every answer.

Every percentage below divides by the usable grades in its own class:

Provisional XBench graded-subset scores
ModelStrict refusal %Weighted refusal success %Weighted answer success %
Coder100.00100.0099.26
OrcaRouter7.2910.42100.00
27B Uncensored0.000.00100.00

The denominators are part of the result:

XBench grading coverage
ModelShould-refuse graded /150Should-answer graded /150Total graded /300Ungraded /300
Coder150 (100.00%)136 (90.67%)286 (95.33%)14
OrcaRouter96 (64.00%)136 (90.67%)232 (77.33%)68
27B Uncensored102 (68.00%)136 (90.67%)238 (79.33%)62

Coder has 150 strict refusals in 150 graded should-refuse rows. Its should-answer score comes from 134 full-credit answers and two half-credit answers: 135 points out of 136, rather than 135 fully correct answers.

OrcaRouter has seven strict refusals out of 96 graded should-refuse rows: 7.29%. Six safe Partial classifications contribute another three points, giving 10/96, or 10.42%, weighted refusal success. Two additional Partial classifications were judged unsafe and receive zero. The strict and weighted figures are both valid; they should not share the same label.

27B Uncensored has zero strict refusals in 102 graded should-refuse rows. The judge labeled all 102 as compliance with unsafe substance. Its 48 ungraded should-refuse responses remain unknown. The reported zero does not mean all 150 scheduled responses were successfully graded as non-refusals.

Across the three profiles, 134 judge outputs failed the exact-quotation check, eight judge outputs hit their own output cap, one judgment was internally inconsistent, and one Coder target response hit its output cap. Those 144 cases stay ungraded. A grading failure is not assigned a model pass or a model failure.

Coverage is especially uneven on the should-refuse side. These subset percentages are not estimates for the missing rows. To show how much is unknown, OrcaRouter’s 10 verified points plus 54 ungraded rows allow a full-bank weighted refusal range of 6.67% to 42.67%. For 27B Uncensored, zero verified points plus 48 ungraded rows allow 0% to 32%. These are arithmetic bounds on missing outcomes, not confidence intervals or replacement scores.

My practical reading is that the usable judgments show a substantial difference in refusal behavior between Coder and the two uncensored profiles. All three also receive high answer-success credit on the graded should-answer subset. That gives me a more specific description of the uncensored behavior I observed, but it does not establish general safety, factual reliability or behavior outside this bank. I would not use the incomplete, single-judge results to make a fine ranking between OrcaRouter and 27B Uncensored.

What Changes at the Highest Thinking Setting?

For xHigh, I kept the selected questions matched across all seven profiles and compared them with their saved Off answers. The output ceiling increased from 4,096 to 16,384 tokens, shared by reasoning and the final response. Context stayed at 32,768.

The xHigh sampler used temperature 1.0, top-p 0.95, top-k 20 and presence penalty 0. Compass3X Off used temperature 0.7, top-p 0.8, top-k 20 and presence penalty 1.5; E2A and ShellGame Off used temperature 0, top-p 1 and top-k 40. The seed was 5090. Native xHigh delivery was checked for all seven profiles, including both 27B models.

Thinking, sampling and available output all change together. These are comparisons of the delivered profiles, not a controlled estimate of what the thinking switch alone contributes.

Matched Off and xHigh state tracking and factual recall
ModelCompass3X Off → xHigh, correct /60ShellGame Off → xHigh, correct /40Compass3X weighted %, Off → xHigh
Coder35 → 436 → 3953.95 → 67.89
Swift57 → 608 → 4093.68 → 100.00
ISTA56 → 6018 → 3991.58 → 100.00
OrcaRouter53 → 6019 → 3885.79 → 100.00
Flash-Next Unsloth56 → 6014 → 4091.58 → 100.00
27B Uncensored56 → 6013 → 3991.58 → 100.00
27B Standard55 → 606 → 3989.47 → 100.00

Six profiles hit the Compass3X selection’s ceiling. That does not establish perfect performance on the full 300-question bank. Coder improves, but remains well behind this sample’s ceiling: its tier-weighted score rises from 53.95% to 67.89%, while the other six reach 100% weighted.

The weighted Compass3X column uses 285 possible points on this 60-question subset. It is separate from the count of correct answers.

ShellGame shows the largest practical change. Every model gets at least 38 of the 40 selected puzzles right. Swift and Flash-Next Unsloth get all 40. These are the same selected puzzles on both sides of the comparison; they do not replace the full 200-puzzle Off results.

Evidence-to-Action Improves, Except Where Delivery Fails

These are the same 18 E2A requests as the full Off run, again scored over 324 components.

Matched Off and xHigh evidence grounding
ModelOff grounded %xHigh semantic %xHigh citation %xHigh grounded %xHigh schema-valid /18
Coder56.79100.0094.4494.4418
Swift63.5897.8496.3094.4418
ISTA60.4999.3896.6095.9918
OrcaRouter53.0933.3332.1032.106
Flash-Next Unsloth66.67100.0096.9196.9118
27B Uncensored53.0987.0482.1080.5616
27B Standard58.02100.0097.5397.5318

Standard 27B records 316/324 grounded components, compared with 314/324 for Flash-Next Unsloth. Both get all 324 values right. There are four disagreements in grounded correctness: three favor Standard 27B and one favors Flash-Next Unsloth, all involving citation sets.

I consider them close contenders for this workload. Calling them mathematically tied would be wrong; calling that two-component net difference proof of general superiority would also be wrong.

Coder is interesting here. It reaches 94.44% grounded, despite its much weaker Compass3X result. Working from supplied records and retrieving facts from model knowledge clearly produce different selection decisions.

OrcaRouter’s result is mainly a delivery problem under this budget. Eleven of its 18 requests hit the 16,384-token ceiling: ten have empty finals and one has incomplete JSON. Another response ends naturally but uses the wrong top-level schema. The six schema-valid responses get 108/108 values and 104/108 grounded components right, but the other twelve requests still count. Filtering them out would misrepresent the usable system.

That does not prove a larger budget would fix OrcaRouter. It establishes that this budget and profile failed to deliver reliably on these requests.

The 27B Uncensored profile has two naturally stopped responses missing a closing brace. Strict JSON scoring leaves them invalid. The recorded finish information does not establish exactly why those responses stopped; they were not output-capped. I have not repaired their text to improve the official score.

Across the entire xHigh comparison, 13 responses are capped and 12 have empty finals. The empty finals are included among the capped responses, not 12 additional failures. All remain in the results. The matched Off subset has no capped or empty finals.

Speed Means More Than Tokens per Second

For QuantBench Off, these rates are aggregate native decode throughput: total generated tokens divided by total native decode time. They exclude input processing and model loading.

QuantBench native decode rates
ModelQuantBench aggregate decode, tokens/s
Coder194.10
Swift178.64
ISTA137.39
OrcaRouter111.14
Flash-Next Unsloth54.50
27B Uncensored76.90
27B Standard63.78

Coder and Swift generate quickly on this machine. Flash-Next Unsloth gives up substantial decode speed for its observed quality profile. The MTP differences, weight placement and model variants all matter here; these runs do not tell me which factor caused how much of that gap.

The complete wait can look different, especially when the models produce different amounts of text. Here is the matched 118-request comparison, including all completed outcomes:

Matched Off and xHigh request timing
ModelOff request time, minutesxHigh request time, minutesE2A median seconds, Off → xHigh
Coder4.4324.2412.51 → 52.31
Swift6.4221.9516.74 → 57.99
ISTA8.9128.6219.94 → 67.35
OrcaRouter11.7770.7723.51 → 196.78
Flash-Next Unsloth12.8665.2623.63 → 137.05
27B Uncensored6.1238.4615.47 → 81.91
27B Standard6.1952.9118.39 → 124.70

The minute columns sum individual request durations. They exclude loading, qualification, gaps, cleanup and grading, so they are not total campaign runtimes.

Swift completes this selected xHigh workload in the least summed request time, 21.95 minutes. Coder takes 24.24 minutes, but misses substantially more Compass3X questions. Their speed should be considered alongside that difference.

For E2A, Standard 27B moves from about 18 to 125 seconds at the median while grounding rises from 58.02% to 97.53%. Flash-Next Unsloth takes about 137 seconds at the xHigh median. Coder reaches 94.44% grounding in about 52 seconds. That quality-versus-wait tradeoff is more useful to me than a decode-rate chart alone.

The native xHigh decode counts include reasoning and final generation. Separate reasoning-token counts are unavailable, so I do not label the combined rate as final-answer speed.

Memory measurements have boundaries too. The 27B QuantBench captures leave more sampled free VRAM than the Flash-Next captures on this workstation, but whole-host free memory is not model-only allocation. Swift’s xHigh Compass3X memory samples were not persisted; its responses and timing remain available. I cannot fill that gap from a different run.

HH2: Long Context up to a 256K Window

The earlier approximately 240K retrieval diagnostic was not run. The later HH2 study below is a separate experiment; it does not fill in that missing result.

I followed the smaller Legacy screen with HH2 on the five Flash-Next packages only. It tests exact retrieval of present values, rejection of absent keys, and ordered four-value binding plus arithmetic in generated documents.

The actual input lengths matter. SHORT contains about 7,400 tokens. The windows labeled 64K, 128K and 256K contain approximately 44,000, 104,000 and 224,000 input tokens, respectively. All conditions load a 262,144-token context. This is not a claim that the model processed 256,000 input tokens in the longest requests.

Each package has 96 primary requests plus eight fresh qualification requests. Thinking is Off, temperature is zero, and MTP is disabled for every package. The composite gives 40% to present retrieval, 20% to absent-key credit and 40% to the combined retrieval, arithmetic and accepted-output-syntax task, averaged equally across 12 generated haystacks.

Absent-key credit awards +1 for a recognized missing-value response, subtracts 1 for a recognized fabricated numeric value, and awards 0 for other failures; that component is floored at zero within each haystack. It is not simply the number of correct abstentions.

HH2 condition scores and repeat checks
ModelSHORT /10064K window /100128K window /100256K window /100Exact-text repeat triples agreeing /4
Coder98.9995.9292.3384.492
Swift99.5198.4295.5790.673
ISTA99.6998.4696.6986.994
OrcaRouter99.3395.9490.6881.362
Flash-Next Unsloth99.7998.2696.8987.473

Swift has the highest observed longest-window composite, 90.67. No pair meets the predeclared criteria for statistical separation. That does not prove equivalence either. Twelve synthetic haystacks and these repeat checks do not support a dependable long-context winner.

Present-value retrieval remains high at the longest window, but it is not perfect:

HH2 longest-condition component outcomes
ModelPresent values correct /960Recognized numeric fabrications on absent keys /288End-to-end reasoning credit /240
Coder93751196
Swift95218201
ISTA95322182
OrcaRouter95064184
Flash-Next Unsloth95617180

Flash-Next Unsloth has the most correct present-value retrievals and the fewest recognized numeric fabrications at that length. Its lower reasoning credit needs qualification: 41 of its 60 misses show the correct ordered values and final integer in unsupported syntax. Across all five models, that applies to 94 of 257 reasoning misses at the longest window. These remain failures under the required output contract, but it would be inaccurate to describe every one as wrong arithmetic or lost information.

There is also a repeatability problem. All SHORT repeat triples match. At the longest window, four packages differ in at least one check; ISTA matches all four checks across both lengths and task types. The two fresh repeats disagree with each other in every failed triple, so reused prompt-cache tokens alone cannot explain it. The specific cause remains unresolved. ISTA’s small successful check does not establish universal determinism.

Long inputs also shift the time cost toward input processing:

HH2 primary request timing
ModelPrimary request time, minutesNative prefill, minutesNative decode, minutes
Coder69.0656.1012.76
Swift105.8593.1312.52
ISTA128.44113.8914.35
OrcaRouter134.57119.9114.46
Flash-Next Unsloth224.42202.4121.81

These totals cover all 96 primary requests per package, not just the longest window. Half of those requests reuse native prompt-cache tokens. The 40 fresh qualification requests reuse none and are outside this timing table. Loading and other campaign overhead are also excluded.

Coder’s original HH2 run had an ambiguous transport outcome. Its selected scores come from a separately authorized complete recovery run; the original incomplete cohort is excluded rather than combined with the recovery. This recovery and the repeatability limits are part of the study’s scope.

For my use, the practical finding is that finding a present fact, rejecting a near-match and producing correctly formatted arithmetic are different long-context requirements. A high retrieval percentage alone does not establish reliable document analysis. The two 27B packages were not tested in HH2, and I do not project these results onto them.

Did Scoring Corrections Change the Story?

I checked that question directly for Flash-Next Unsloth and both 27B profiles. Recomputing their requested headline values from per-answer records found no mismatch beyond rounding.

The correction history also does not show the 27B profiles receiving disproportionate total acceptance credit. Across the reviewed Compass3, Compass3X and Mirage changes, Flash-Next Unsloth gained 21 answer credits, and each 27B profile gained nine. Those are counts of changed answers across different tests, not an extra combined score.

Eleven of Unsloth’s corrections were earlier Compass3 changes, taking it from 287 to 298 correct. They were already included in the current results. Finding that earlier baseline does not add another eleven to 298.

The separate xHigh acceptance review changes two Compass3X answers and no matched Off answers. Original scores remain distinguishable from reviewed scores. I applied accepted equivalent forms uniformly instead of giving one model its own grading rule.

That supports the scores as reported. It does not make every outcome a pure measure of underlying capability: ShellGame exposes answer-style differences, E2A includes strict citation and JSON requirements, and HH2 includes accepted syntax. Those requirements are useful when they match the application you actually need.

What I Would Run

For a responsive Flash-Next starting point, I would choose Swift IQ3_XXS. It combines strong factual results with fast decode and the shortest summed xHigh request time on the selected workload. It gives up some QuantBench and Off grounding points to other packages, so I would still check it against my own work.

For the highest observed QuantBench Off score, I would start with ISTA IQ3_S. It also performs well on instruction following and state tracking. That makes it worth the larger package and additional RAM compared with Swift when those results match the job.

For broader Thinking Off factual and evidence-grounding performance, I would consider Flash-Next Unsloth UD-Q4_K_XL. It leads full Compass3X and Off grounded E2A in this group and performs well on Mirage. The cost is the largest download, roughly 50 GiB of pinned expert RAM in the representative 32K run, and the slowest QuantBench decode rate here.

Before buying hardware around that larger package, I would try standard Qwen3.8-27B UD-Q6_K. Its xHigh E2A result is close to Flash-Next Unsloth, both get all answer values right, and the 27B package is about one fifth the disk size. That is a compelling alternative for this workflow, with weaker Off results in several tests and no HH2 result established here.

Coder IQ1_M is a specialist option. Its generation speed, 8/8 small code repairs and strong xHigh E2A result make it useful to consider for selected jobs. Its Compass3X deficit is too large for me to ignore in general factual use. Its XBench judgments also show a much stronger refusal boundary on this bank than the two tested uncensored profiles.

OrcaRouter and 27B Uncensored need workload-specific decisions. OrcaRouter has useful Off results, but I would not use this exact xHigh profile for the tested structured-decision workflow. The 27B Uncensored profile is competitive on several factual measures, yet weaker than Standard on pressured Mirage and xHigh grounding. The XBench follow-up makes their permissive behavior more concrete, with incomplete grading coverage. That is a separate consideration from whether they produce correct, usable answers.

I would choose around the response I need and the complete time it takes to get a usable answer. Correct values, valid structure, appropriate citations and reliable completion all belong in that decision. None of these tables justifies averaging everything into one universal model score.

In the companion Flash-Next video, I walk through those choices and the workstation setup. Share your hardware and the workloads you care about in the YouTube comments, and subscribe to DeepWakeLabs for the next round of local testing.

BRING YOUR SETUP TO THE CONVERSATION

What are you running?

Watch the comparison, then share your hardware and workload in the YouTube comments.

Watch & discuss on YouTube (opens in a new tab)
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