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BenchLM
Model A
Qwen3.5 397B

Alibaba

57.3/100

Estimated · Public rank #81

90% interval 45.8–68.8

Qwen3.5 397B vs Qwen3.8-27B

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.8-27B

Alibaba

—

Evidence status unavailable

90% interval unavailable

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.

6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    Qwen3.8-27B

    Qwen3.8-27B 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-27B 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
6
Qwen3.5 397B only
32
Qwen3.8-27B only
21
Like-for-like categories
0 / 8

3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Directional only
Qwen3.5 397B
66.5
Qwen3.8-27B
61.7
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
Qwen3.5 397B
56.6
Qwen3.8-27B
38.7
Weighted basis
4 vs 2 rows
Reading
Directional only

Multimodal

Directional only
Qwen3.5 397B
79.6
Qwen3.8-27B
90.2
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Qwen3.5 397B
56.5
Qwen3.8-27B
84.3
Weighted basis
2 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.5 397B
63.2
Qwen3.8-27B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Qwen3.5 397B
90.6
Qwen3.8-27B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.5 397B
84.7
Qwen3.8-27B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.5 397B
92.6
Qwen3.8-27B
79.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

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

Qwen3.5 397B
$0.0024
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.8-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5 397B
$0.0408
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.8-27B has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.5 397B
$0.168
Does not fit in one request
Cached input priced at the published list-input rate
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-27B 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.

Context window

Maximum documented context; output-token limits may be lower.

Qwen3.5 397B

128K

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

Qwen3.5 397B

Not sourced

Qwen3.8-27B

Not sourced

Cached-input rate

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

Qwen3.5 397B

Not published

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

Qwen3.5 397B

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

Qwen3.5 397B

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

Qwen3.5 397B

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

Qwen3.5 397B

Non-Reasoning

Qwen3.8-27B

Reasoning

Weight access

Qwen3.5 397B

Open Weight

Qwen3.8-27B

Open Weight

License

Qwen3.5 397B

Open Weight

Qwen3.8-27B

Open Weight

Release date

Qwen3.5 397B

2026-02-16

Qwen3.8-27B

2026-08-05

If you already use one of these models
Deployment change
Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
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
Qwen3.8-27B has the larger documented window (262K).

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 evidence59 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.5 397B52.5%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • BrowseComp

    Qwen3.5 397B62%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Claw-Eval

    Qwen3.5 397B56.8%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • QwenClawBench

    Qwen3.5 397B51.8%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • τ³-bench results

    Qwen3.5 397B68.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • VITA-Bench

    Qwen3.5 397B43.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • DeepPlanning

    Qwen3.5 397B37.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Toolathlon

    Qwen3.5 397B36.3%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MCP Atlas

    Qwen3.5 397B46.1%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MCP-Tasks

    Qwen3.5 397B74.2%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • WideResearch

    Qwen3.5 397B74.0%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Gert Labs

    Qwen3.5 397B46.76%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • ResearchClawBench

    Qwen3.5 397B14.2%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Terminal-Bench 2.1

    Qwen3.5 397B—
    Qwen3.8-27B73.0%
    Source

    Not directly comparable

  • CoWorkBench

    Qwen3.5 397B—
    Qwen3.8-27B70.7%
    Source

    Not directly comparable

  • JobBench

    Qwen3.5 397B—
    Qwen3.8-27B33.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    Qwen3.5 397B—
    Qwen3.8-27B42.9%
    Source

    Not directly comparable

  • OSWorld-Verified

    Qwen3.5 397B—
    Qwen3.8-27B84.3%
    Source

    Not directly comparable

  • WebArena-Verified

    Qwen3.5 397B—
    Qwen3.8-27B64.8%
    Source

    Not directly comparable

  • AndroidWorld

    Qwen3.5 397B—
    Qwen3.8-27B81.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.5 397B76.2%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • LiveCodeBench v6

    Qwen3.5 397B83.6%
    Source
    Qwen3.8-27B90.3%
    Source

    Qwen3.8-27B leads this result

  • SWE-bench Pro

    Qwen3.5 397B50.9%
    Source
    Qwen3.8-27B61.7%
    Source

    Qwen3.8-27B leads this result

  • Terminal-Bench 2.1

    Qwen3.5 397B—
    Qwen3.8-27B73.0%
    Source

    Not directly comparable

  • NL2Repo

    Qwen3.5 397B—
    Qwen3.8-27B42.3%
    Source

    Not directly comparable

  • deepSwe

    Qwen3.5 397B—
    Qwen3.8-27B42.2%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Qwen3.5 397B63.2%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • AI-Needle

    Qwen3.5 397B68.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

Knowledge

  • GPQA

    Qwen3.5 397B88.4%
    Source
    Qwen3.8-27B89.2%
    Source

    Qwen3.8-27B leads this result

  • SuperGPQA

    Qwen3.5 397B70.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MMLU-Pro

    Qwen3.5 397B87.8%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MMLU-Redux

    Qwen3.5 397B94.9%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • C-Eval

    Qwen3.5 397B93%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • HLE

    Qwen3.5 397B28.7%
    Source
    Qwen3.8-27B30.8%
    Source

    Qwen3.8-27B leads this result

  • GPQA-D

    Qwen3.5 397B—
    Qwen3.8-27B89.2%
    Source

    Not directly comparable

  • HLE w/o tools

    Qwen3.5 397B—
    Qwen3.8-27B30.8%
    Source

    Not directly comparable

Math

  • AIME26

    Qwen3.5 397B93.3%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • HMMT Feb 2025

    Qwen3.5 397B94.8%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • HMMT Nov 2025

    Qwen3.5 397B92.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • HMMT Feb 2026

    Qwen3.5 397B87.9%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MMAnswerBench

    Qwen3.5 397B80.9%
    Source
    Qwen3.8-27B—

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen3.5 397B84.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • NOVA-63

    Qwen3.5 397B59.1%
    Source
    Qwen3.8-27B—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Qwen3.5 397B79%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MathVision

    Qwen3.5 397B88.6%
    Source
    Qwen3.8-27B90.0%
    Source

    Qwen3.8-27B leads this result

  • CharXiv

    Qwen3.5 397B80.8%
    Source
    Qwen3.8-27B90.2%
    Source

    Qwen3.8-27B leads this result

  • VideoMMMU

    Qwen3.5 397B84.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • ScreenSpot Pro

    Qwen3.5 397B65.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • V*

    Qwen3.5 397B95.8%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • MathVision w/ Python

    Qwen3.5 397B—
    Qwen3.8-27B94.6%
    Source

    Not directly comparable

  • BabyVision

    Qwen3.5 397B—
    Qwen3.8-27B65.7%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Qwen3.5 397B—
    Qwen3.8-27B85.6%
    Source

    Not directly comparable

  • Vision2Web

    Qwen3.5 397B—
    Qwen3.8-27B62.9%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Qwen3.5 397B—
    Qwen3.8-27B83.7%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Qwen3.5 397B—
    Qwen3.8-27B91.1%
    Source

    Not directly comparable

  • RealWorldQA

    Qwen3.5 397B—
    Qwen3.8-27B85.9%
    Source

    Not directly comparable

  • ERQA

    Qwen3.5 397B—
    Qwen3.8-27B65.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Qwen3.5 397B92.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • IFBench

    Qwen3.5 397B—
    Qwen3.8-27B79.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.5 397B or Qwen3.8-27B?

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, Qwen3.5 397B or Qwen3.8-27B?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Qwen3.5 397B or Qwen3.8-27B?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Qwen3.5 397B or Qwen3.8-27B?

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, Qwen3.5 397B or Qwen3.8-27B?

Qwen3.8-27B has the larger documented context window: 262K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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