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BenchLM

Claude Sonnet 4.6 vs Qwen3.8-Omni-Flash

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Anthropic logo
Model A
Claude Sonnet 4.6

Anthropic

62.86/100

Supported · Public rank #48

90% interval 51.0–74.8

Alibaba logo
Model B
Qwen3.8-Omni-Flash

Alibaba

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.8-Omni-Flash

    Qwen3.8-Omni-Flash has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Qwen3.8-Omni-Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Qwen3.8-Omni-Flash is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash has no comparable published API token rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
3
Claude Sonnet 4.6 only
23
Qwen3.8-Omni-Flash only
16
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Directional only
Claude Sonnet 4.6
52.2
Supported · #49/154
Qwen3.8-Omni-Flash
53.5
Estimated · #45/154
Basis
BenchAlign lane · 8 vs 5 public rows
Reading
Directional only

Knowledge

Directional only
Claude Sonnet 4.6
55.7
Supported · #51/184
Qwen3.8-Omni-Flash
54.9
Estimated · #54/184
Basis
BenchAlign lane · 6 vs 3 public rows
Reading
Directional only

Instruction following

Directional only
Claude Sonnet 4.6
46.6
#88/124
Qwen3.8-Omni-Flash
87.7
#29/124
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Agentic

Not comparable
Claude Sonnet 4.6
43.8
Supported · #95/154
Qwen3.8-Omni-Flash
Not ranked
Basis
BenchAlign lane · 9 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Sonnet 4.6
67.9
Unranked · 2 rankable rows
Qwen3.8-Omni-Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 4.6
48.8
Unranked · 2 rankable rows
Qwen3.8-Omni-Flash
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 4.6
Not ranked
Qwen3.8-Omni-Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 4.6
54.1
#33/48
Qwen3.8-Omni-Flash
85.1
Unranked · 7 rankable rows
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Sonnet 4.6
$0.0105
Fits in one request
Qwen3.8-Omni-Flash
API rate not published
Fits in one request

Qwen3.8-Omni-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 4.6
$0.195
Fits in one request
Qwen3.8-Omni-Flash
API rate not published
Fits in one request

Qwen3.8-Omni-Flash has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Sonnet 4.6
$0.81
Does not fit in one request
Cached input priced at the published list-input rate
Qwen3.8-Omni-Flash
API rate not published
Fits in one request
Cached-input rate unavailable

Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Omni-Flash has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Sonnet 4.6

Not published

Qwen3.8-Omni-Flash

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Claude Sonnet 4.6

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Documented outputs

Claude Sonnet 4.6

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Provider availability

Claude Sonnet 4.6

Not sourced

Qwen3.8-Omni-Flash

Not sourced

Reasoning profile

Claude Sonnet 4.6

Non-Reasoning

Qwen3.8-Omni-Flash

Reasoning

Weight access

Claude Sonnet 4.6

Proprietary

Qwen3.8-Omni-Flash

Proprietary

License

Claude Sonnet 4.6

Proprietary

Qwen3.8-Omni-Flash

Proprietary

Release date

Claude Sonnet 4.6

2026-02-01

Qwen3.8-Omni-Flash

2026-09-18

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.8-Omni-Flash has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence42 rows

Agentic

  • Terminal-Bench 2.0

    Claude Sonnet 4.659.1%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 4.672.1%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • Claw-Eval

    Claude Sonnet 4.667.8%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • CyberGym

    Claude Sonnet 4.665.2%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • Gert Labs

    Claude Sonnet 4.662.92%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • OSWorld 2.0

    Claude Sonnet 4.68.3%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • JobBench

    Claude Sonnet 4.636.9%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Sonnet 4.657.3%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • ApprenticeBench

    Claude Sonnet 4.62%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • CoWorkBench

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash75.3%
    Source

    Not directly comparable

  • AndroidWorld

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash87.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 4.679.6%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • SWE-Rebench

    Claude Sonnet 4.660.7%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • React Native Evals

    Claude Sonnet 4.680.6%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • Vibe Code Bench

    Claude Sonnet 4.651.48%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • cursorBench31

    Claude Sonnet 4.648.8%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Sonnet 4.624.3%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Sonnet 4.682.1%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Sonnet 4.677.4%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • SWE-bench Pro

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash63.3%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash80.5%
    Source

    Not directly comparable

  • NL2Repo

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash48.9%
    Source

    Not directly comparable

  • DeepSWE

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash57.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash92.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Sonnet 4.689.9%
    Source
    Qwen3.8-Omni-Flash91%
    Source

    Qwen3.8-Omni-Flash leads this result

  • SuperGPQA

    Claude Sonnet 4.695%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • MMLU-Pro

    Claude Sonnet 4.679.2%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • HLE

    Claude Sonnet 4.649%
    Source
    Qwen3.8-Omni-Flash36.5%
    Source

    Claude Sonnet 4.6 leads this result

  • GPQA Diamond (Vals)

    Claude Sonnet 4.685.6%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Sonnet 4.687.3%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • GPQA-D

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash91.0%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Sonnet 4.632.400%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Sonnet 4.68.300%
    Source
    Qwen3.8-Omni-Flash—

    Not directly comparable

Multimodal

  • CharXiv

    Claude Sonnet 4.677.4%
    Source
    Qwen3.8-Omni-Flash91.4%
    Source

    Qwen3.8-Omni-Flash leads this result

  • Vision2Web

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash62.9%
    Source

    Not directly comparable

  • ERQA

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash71.0%
    Source

    Not directly comparable

  • LVBench

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash76.9%
    Source

    Not directly comparable

  • RealWorldQA

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash87.7%
    Source

    Not directly comparable

  • MathVision

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash91.8%
    Source

    Not directly comparable

  • MathVision w/ Python

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash96.2%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash83.5%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Claude Sonnet 4.6—
    Qwen3.8-Omni-Flash81.5%
    Source

    Not directly comparable

Questions

Which is better, Claude Sonnet 4.6 or Qwen3.8-Omni-Flash?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Sonnet 4.6 or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash scores higher for coding on the public lane, 53.5 to 52.2. Qwen3.8-Omni-Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Sonnet 4.6 or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Sonnet 4.6 or Qwen3.8-Omni-Flash?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Claude Sonnet 4.6 or Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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