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Gemma 4 26B A4B vs Qwen3.7 Max

Updated October 2, 2026. Rank says Qwen3.7 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Qwen3.7 Max has the higher public score estimate, 63.46 versus 46.89, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemma 4 26B A4B

Google

46.89/100

Supported · Public rank #104

90% interval 32.3–61.5

Model B
Alibaba logo
Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results
2
Gemma 4 26B A4B only
2
Qwen3.7 Max only
39
Like-for-like categories
2 / 8
Supported: Gemma 4 26B A4B and Qwen3.7 MaxHow the comparison works

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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Qwen3.7 Max

    Qwen3.7 Max has the higher public coding point estimate, 45.4 to 33.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.7 Max

    Qwen3.7 Max has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • Agentic work

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

    Not enough matched evidence

    Gemma 4 26B A4B 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

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

    Confidence: listed-rates

  • 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

33.8Gemma 4 26B A4B45.4Qwen3.7 Max

Like-for-like · BenchAlign v5.8

Qwen3.7 Max has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Like-for-like
Gemma 4 26B A4B
33.8
Supported · #86/144
Qwen3.7 Max
45.4
Supported · #54/144
Basis
BenchAlign v5.8 lane · 0 vs 10 public rows
Reading
Qwen3.7 Max leads · intervals overlap

Knowledge

Like-for-like
Gemma 4 26B A4B
36.5
Supported · #108/171
Qwen3.7 Max
59.8
Supported · #41/171
Basis
BenchAlign v5.8 lane · 3 vs 9 public rows
Reading
Qwen3.7 Max leads

Instruction following

Directional only
Gemma 4 26B A4B
87.3
#30/125
Qwen3.7 Max
89.2
#17/125
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Agentic

Not comparable
Gemma 4 26B A4B
Not ranked
Qwen3.7 Max
39.3
Supported · #60/119
Basis
BenchAlign v5.8 lane · 0 vs 10 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemma 4 26B A4B
67.4
Unranked · 2 rankable rows
Qwen3.7 Max
76.3
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemma 4 26B A4B
45.2
#42/49
Qwen3.7 Max
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 26B A4B
Not ranked
Qwen3.7 Max
100.0
#1/16
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Gemma 4 26B A4B
Not ranked
Qwen3.7 Max
81.9
Unranked · 3 rankable rows
Basis
Provisional lane · 0 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 v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

Gemma 4 26B A4B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.7 Max
API rate not published
Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 26B A4B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.7 Max
API rate not published
Fits in one request

Gemma 4 26B A4B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 26B A4B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.7 Max
API rate not published
Fits in one request
Cached-input rate unavailable

Gemma 4 26B A4B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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.

Gemma 4 26B A4B

No comparable hosted API rate

Qwen3.7 Max

No comparable hosted API rate

Provider availability

Gemma 4 26B A4B

Generally Available · Gemini API, Google AI Studio, open weights

Google Gemma Gemini API guide

Qwen3.7 Max

Not sourced

Reasoning profile

Gemma 4 26B A4B

Reasoning

Qwen3.7 Max

Reasoning

Weight access

Gemma 4 26B A4B

Open Weight

Qwen3.7 Max

Proprietary

License

Gemma 4 26B A4B

Open Weight

Qwen3.7 Max

Proprietary

Release date

Gemma 4 26B A4B

2026-04-02

Qwen3.7 Max

2026-05-16

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
Qwen3.7 Max has the higher public score estimate, 63.46 versus 46.89, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.7 Max has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 26B A4B or Qwen3.7 Max?

Qwen3.7 Max has the higher public score estimate, 63.46 versus 46.89, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemma 4 26B A4B or Qwen3.7 Max?

Qwen3.7 Max has the higher public coding point estimate, 45.4 to 33.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemma 4 26B A4B or Qwen3.7 Max?

Gemma 4 26B A4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 26B A4B or Qwen3.7 Max?

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, Gemma 4 26B A4B or Qwen3.7 Max?

Qwen3.7 Max has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence43 rows

Agentic

  • Terminal-Bench 2.0

    Gemma 4 26B A4B—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • QwenClawBench

    Gemma 4 26B A4B—
    Qwen3.7 Max64.3%
    Source

    Not directly comparable

  • Claw-Eval

    Gemma 4 26B A4B—
    Qwen3.7 Max65.2%
    Source

    Not directly comparable

  • BFCL v4

    Gemma 4 26B A4B—
    Qwen3.7 Max75.0%
    Source

    Not directly comparable

  • MCP Atlas

    Gemma 4 26B A4B—
    Qwen3.7 Max76.4%
    Source

    Not directly comparable

  • VITA-Bench

    Gemma 4 26B A4B—
    Qwen3.7 Max47.9%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemma 4 26B A4B—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • Gert Labs

    Gemma 4 26B A4B—
    Qwen3.7 Max64.27%
    Source

    Not directly comparable

  • ResearchClawBench

    Gemma 4 26B A4B—
    Qwen3.7 Max18.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemma 4 26B A4B—
    Qwen3.7 Max61.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Gemma 4 26B A4B—
    Qwen3.7 Max80.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemma 4 26B A4B—
    Qwen3.7 Max60.6%
    Source

    Not directly comparable

  • SWE Multilingual

    Gemma 4 26B A4B—
    Qwen3.7 Max78.3%
    Source

    Not directly comparable

  • NL2Repo

    Gemma 4 26B A4B—
    Qwen3.7 Max47.2%
    Source

    Not directly comparable

  • SciCode

    Gemma 4 26B A4B—
    Qwen3.7 Max53.5%
    Source

    Not directly comparable

  • LiveCodeBench

    Gemma 4 26B A4B—
    Qwen3.7 Max91.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Gemma 4 26B A4B—
    Qwen3.7 Max69.7%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Gemma 4 26B A4B—
    Qwen3.7 Max53.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemma 4 26B A4B—
    Qwen3.7 Max87.1%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Gemma 4 26B A4B—
    Qwen3.7 Max68.8%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemma 4 26B A4B—
    Qwen3.7 Max90.4%
    Source

    Not directly comparable

  • CritPt

    Gemma 4 26B A4B—
    Qwen3.7 Max13.4%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemma 4 26B A4B73.8%
    Source
    Qwen3.7 Max—

    Not directly comparable

Knowledge

  • MMLU-Pro

    Gemma 4 26B A4B82.6%
    Source
    Qwen3.7 Max89.6%
    Source

    Qwen3.7 Max leads this result

  • HLE

    Gemma 4 26B A4B17.2%
    Source
    Qwen3.7 Max41.4%
    Source

    Qwen3.7 Max leads this result

  • HLE w/o tools

    Gemma 4 26B A4B8.7%
    Source
    Qwen3.7 Max—

    Not directly comparable

  • GPQA

    Gemma 4 26B A4B—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • GPQA-D

    Gemma 4 26B A4B—
    Qwen3.7 Max92.4%
    Source

    Not directly comparable

  • MMLU-Redux

    Gemma 4 26B A4B—
    Qwen3.7 Max95%
    Source

    Not directly comparable

  • SuperGPQA

    Gemma 4 26B A4B—
    Qwen3.7 Max73.6%
    Source

    Not directly comparable

  • MMMLU

    Gemma 4 26B A4B—
    Qwen3.7 Max90.3%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemma 4 26B A4B—
    Qwen3.7 Max90.2%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemma 4 26B A4B—
    Qwen3.7 Max89.3%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Gemma 4 26B A4B—
    Qwen3.7 Max87%
    Source

    Not directly comparable

  • NOVA-63

    Gemma 4 26B A4B—
    Qwen3.7 Max59.0%
    Source

    Not directly comparable

  • INCLUDE

    Gemma 4 26B A4B—
    Qwen3.7 Max86.2%
    Source

    Not directly comparable

  • MAXIFE

    Gemma 4 26B A4B—
    Qwen3.7 Max89.2%
    Source

    Not directly comparable

  • PolyMath

    Gemma 4 26B A4B—
    Qwen3.7 Max86.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Gemma 4 26B A4B—
    Qwen3.7 Max94.3%
    Source

    Not directly comparable

  • IFBench

    Gemma 4 26B A4B—
    Qwen3.7 Max79.1%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    Gemma 4 26B A4B—
    Qwen3.7 Max97.1%
    Source

    Not directly comparable

  • IMOAnswerBench

    Gemma 4 26B A4B—
    Qwen3.7 Max90.0%
    Source

    Not directly comparable

  • Apex

    Gemma 4 26B A4B—
    Qwen3.7 Max44.5%
    Source

    Not directly comparable

43 public results · 2 shared

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Last updated October 2, 2026