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BenchLM
Data

Data as of October 14, 2026 · How the score is built

EstablishedOpen WeightReasoning

Released Apr 15, 2026262K context

Qwen3.6-35B-A3B

Decision readingQwen3.6-35B-A3B scores 43.5 out of 100 and ranks #122 of 221. This profile shows 42 source-displayable benchmark rows; its strongest eligible category is Mathematics at #10. No comparable first-party API price is published in the catalog.

Released Apr 15, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

43.5/100

field median 49.8#122 of 221 ranked models

Public

#122of 221

Verified —

Price

Not listed

input median $0.80No comparable first-party hosted token rate

Speed

129tok/s

field median 90 tok/sFirst token 43.73 s

Context

262Ktokens

field median 256,000Reported for this model; direct source link not stored

Strongest published evidence

Mathematics ranks #10. Particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Validate before choosing

42 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Source-linked · 42 displayable benchmark rows

Follow model changes

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #90 of 124Percentile 28thWeight 22%11 benchmarksVerified
29.2
CodingRank #89 of 149Percentile 41stWeight 20%6 benchmarksVerified
31.8
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalRank #40 of 54Percentile 26thWeight 12%14 benchmarksVerified
66.4
KnowledgeRank #105 of 181Percentile 42ndWeight 12%5 benchmarksVerified
42.9
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathRank #10 of 12Percentile 18thWeight 5%6 benchmarksVerified
58.4

42 of 670 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic11/11 verified
  2. Coding6/6 verified
  3. ReasoningNot measured
  4. Multimodal14/14 verified
  5. Knowledge5/5 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. Math6/6 verified
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

Qwen3.6-35B-A3B category percentile values

  • Agentic28th percentile
  • Coding41st percentile
  • ReasoningNot eligible
  • Multimodal26th percentile
  • Knowledge42nd percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • Math18th percentile

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#90/124
  2. Coding#89/149
  3. ReasoningNot ranked
  4. Multimodal#40/54
  5. Knowledge#105/181
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. Math#10/12
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding6 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore49.5%Versus best verified row

Best verified: Claude Opus 5.5 · 89.9%

Gap40.4 behindWeight26% ref. weight
SWE MultilingualScore67.2%Versus best verified row

Best verified: Claude Opus 5.5 · 93.9%

Gap26.7 behindWeight5% ref. weight
Terminal-Bench 2.0Score51.5%Versus best verified row

Best verified: GPT-5.5 · 82.0%

Gap30.5 behindWeightScored in Agentic
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore73.4%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap22.6 behindWeightDisplay only
LiveCodeBench v6Score80.4%Versus best verified row

Best verified: Sakana Fugu-Ultra · 93.2%

Gap12.8 behindWeightDisplay only
NL2RepoScore29.4%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 65.4%

Gap36 behindWeightDisplay only
Agentic11 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.0Score51.5%Versus best verified row

Best verified: GPT-5.5 · 82%

Gap30.5 behindWeight6% ref. weight
MCP AtlasScore62.8%Versus best verified row

Best verified: Muse Spark 1.1 · 88.1%

Gap25.3 behindWeight4% ref. weight
ToolathlonScore26.9%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap48.7 behindWeight3% ref. weight
τ³-bench resultsτ³-Bench Tool-Agent-User EvaluationScore67.2%Versus best verified row

Best verified: Mercury 2.5 · 96.0%

Gap28.8 behindWeight2% ref. weight
Claw-EvalScore68.7%Versus best verified row

Best verified: Ornith-1.5-397B · 81.4%

Gap12.7 behindWeightDisplay only
QwenClawBenchScore52.6%Versus best verified row

Best verified: Qwen3.7 Max · 64.3%

Gap11.7 behindWeightDisplay only
QwenWebBenchScore1397Versus best verified row

Best verified: Qwen3.7 Max · 1568

Gap171 behindWeightDisplay only
VITA-BenchScore35.6%Versus best verified row

Best verified: Qwen3.7 Max · 47.9%

Gap12.3 behindWeightDisplay only
DeepPlanningScore25.9%Versus best verified row

Best verified: Qwen3.7 Plus · 62.3%

Gap36.4 behindWeightDisplay only
WideResearchScore60.1%Versus best verified row

Best verified: Hy4 preview · 83.9%

Gap23.8 behindWeightDisplay only
Gert LabsGert Labs Composite Game BenchmarkScore42.65%Versus best verified row

Best verified: Claude Opus 4.8 · 72.97%

Gap30.3 behindWeightDisplay only
Benchmark exact
Multimodal14 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMU-ProMassive Multi-discipline Multimodal Understanding ProScore75.3%Versus best verified row

Best verified: Gemini 3.5 Flash · 83.6%

Gap8.3 behindWeightWeighted 40%
CharXivCharXiv ReasoningScore78%Versus best verified row

Best verified: Qwen3.8 Max · 93.5%

Gap15.5 behindWeightWeighted 20%
MMMUMassive Multi-discipline Multimodal UnderstandingScore81.7%Versus best verified row

Best verified: Qwen3.5 397B · 85.0%

Gap3.3 behindWeightDisplay only
RealWorldQAScore85.3%Versus best verified row

Best verified: Qwen3.8-Flash-Next · 88.5%

Gap3.2 behindWeightDisplay only
OmniDocBench 1.5Score89.9%Versus best verified row

Best verified: Qwen3.8 Max · 92.1%

Gap2.2 behindWeightDisplay only
SimpleVQAScore58.9%Versus best verified row

Best verified: Qwen3.7 Plus · 81.7%

Gap22.8 behindWeightDisplay only
CC-OCRScore81.9%Versus best verified row

Best verified: Qwen3.5 397B · 82.0%

Gap0.1 behindWeightDisplay only
AI2D_TESTAI2D test splitScore92.7%Versus best verified row

Best verified: Qwen3.5 397B · 93.9%

Gap1.2 behindWeightDisplay only
RefCOCO (avg)RefCOCO averageScore92.0%Versus best verified row

Best verified: Qwen3.6-27B · 92.5%

Gap0.5 behindWeightDisplay only
ODINW13Score50.8%Versus best verified row

Best verified: Qwen3.7 Plus · 51.1%

Gap0.3 behindWeightDisplay only
Video-MME (with subtitle)Video-MME with subtitleScore86.6%Versus best verified row

Best verified: Qwen3.8 Max · 90.4%

Gap3.8 behindWeightDisplay only
Video-MME (w/o subtitle)Video-MME without subtitleScore82.5%Versus best verified row

Best verified: Qwen3.5 397B · 83.7%

Gap1.2 behindWeightDisplay only
VideoMMMUScore83.7%Versus best verified row

Best verified: Qwen3.8 Max · 88.7%

Gap5 behindWeightDisplay only
MLVU (M-Avg)MLVU mean averageScore86.2%Versus best verified row

Best verified: Qwen3.8 Max · 90.8%

Gap4.6 behindWeightDisplay only
Knowledge5 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore21.4%Versus best verified row

Best verified: Claude Fable 5.1 · 65%

Gap43.6 behindWeight44% ref. weight
MMLU-ProMassive Multitask Language Understanding ProfessionalScore85.2%Versus best verified row

Best verified: Qwen3.7 Max · 89.6%

Gap4.4 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore86%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap10 behindWeight3% ref. weight
SuperGPQASuperGPQA: Scaling LLM Evaluation Across 285 Graduate DisciplinesScore64.7%Versus best verified row

Best verified: Qwen 3.6 Max (preview) · 73.9%

Gap9.2 behindWeight2% ref. weight
C-EvalScore90%Versus best verified row

Best verified: Qwen3.6 Plus · 93.3%

Gap3.3 behindWeightDisplay only
Math6 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score20.351%Versus best verified row

Best verified: GPT-6 Astra · 93.684%

Gap73.3 behindWeightWeighted 30%
HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026Score83.6%Versus best verified row

Best verified: Qwen3.7 Max · 97.1%

Gap13.5 behindWeightWeighted 25%
AIME26AIME 2026Score92.7%Versus best verified row

Best verified: GLM-5.2 · 99.2%

Gap6.5 behindWeightWeighted 25%
HMMT Feb 2025Harvard-MIT Mathematics Tournament February 2025Score90.7%Versus best verified row

Best verified: GLM-4.7 · 97.1%

Gap6.4 behindWeightDisplay only
HMMT Nov 2025Harvard-MIT Mathematics Tournament November 2025Score89.1%Versus best verified row

Best verified: Qwen3.6 Plus · 94.6%

Gap5.5 behindWeightDisplay only
MMAnswerBenchScore78.9%Versus best verified row

Best verified: GLM-5.2 · 91.0%

Gap12.1 behindWeightDisplay only

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Apr 15, 2026 · you are here

    Qwen3.6-35B-A3B

    Score 43.5 · Price not listed

Base entry

Radar

Qwen3.6-35B-A3B release history

Full release history

Radar confirmed these at the source. Use Qwen3.6-35B-A3B in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
Not published
Context window
262K
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Not sourced yet
Cloud regions
Not tracked yet
Lifecycle
Established
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing record
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

Qwen3.6-35B-A3B ranks #122 of 221 on the public leaderboard with a score of 43.54/100. It does not yet have enough sourced coverage for a verified position.

Qwen3.6-35B-A3B is a open weight model with a 262K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Official exact-value snapshot from the Qwen/Qwen3.6-35B-A3B Hugging Face model card. The public repository was created on April 15, 2026, and the model card states a native 262,144-token context window extensible to 1,010,000 tokens.

42 of 670 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Mathematics at #10, while its lowest eligible position is Knowledge at #105. particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Last updated October 14, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Questions

How does Qwen3.6-35B-A3B perform overall in AI benchmarks?

Qwen3.6-35B-A3B ranks #122 out of 221 models on the public BenchAlign leaderboard, with a score of 43.54/100. Its evidence status is Estimated, and this profile shows 42 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is Qwen3.6-35B-A3B good for knowledge and understanding?

Qwen3.6-35B-A3B ranks #105 out of 181 eligible models for knowledge and understanding, with a public category score of 42.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Qwen3.6-35B-A3B good for coding and programming?

Qwen3.6-35B-A3B ranks #89 out of 149 eligible models for coding and programming, with a public category score of 31.8/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Qwen3.6-35B-A3B good for mathematics?

Qwen3.6-35B-A3B ranks #10 out of 12 eligible models for mathematics, with a public category score of 58.4/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Qwen3.6-35B-A3B good for agentic tool use and computer tasks?

Qwen3.6-35B-A3B ranks #90 out of 124 eligible models for agentic tool use and computer tasks, with a public category score of 29.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Qwen3.6-35B-A3B good for multimodal and grounded tasks?

Qwen3.6-35B-A3B ranks #40 out of 54 eligible models for multimodal and grounded tasks, with a public category score of 66.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Qwen3.6-35B-A3B open source?

Qwen3.6-35B-A3B is an open-weight model from Alibaba. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Does Qwen3.6-35B-A3B have full benchmark coverage on BenchLM?

No. Qwen3.6-35B-A3B currently has 58 source-displayable rows across 670 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of Qwen3.6-35B-A3B?

Qwen3.6-35B-A3B has a reported context window of 262K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.

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Compare Qwen3.6-35B-A3B with every tracked model931 comparisons