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Model A
Gemini 3.6 Flash

Google

70.11/100

Supported · Public rank #21

90% interval 63.775.9

Gemini 3.6 Flash vs Qwen3 Max

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Alibaba logo
Model B
Qwen3 Max

Alibaba

43.82/100

Estimated · Public rank #173

90% interval 38.149.6

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

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

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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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Qwen3 Max 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 Max is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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

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
0
Gemini 3.6 Flash only
8
Qwen3 Max only
2
Like-for-like categories
0 / 8

2 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
Gemini 3.6 Flash
58.9
Supported · #30/183
Qwen3 Max
45.4
Estimated · #107/183
Basis
BenchAlign lane · 4 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 3.6 Flash
68.6
Supported · #18/181
Qwen3 Max
43.8
Estimated · #123/181
Basis
BenchAlign lane · 2 vs 0 public rows
Reading
Directional only

Agentic

Not comparable
Gemini 3.6 Flash
50.7
Supported · #60/151
Qwen3 Max
Not ranked
Basis
BenchAlign lane · 2 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.6 Flash
77.8
Unranked · 2 rankable rows
Qwen3 Max
55.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.6 Flash
Not ranked
Qwen3 Max
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.6 Flash
Not ranked
Qwen3 Max
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.6 Flash
82.3
Unranked · 1 rankable row
Qwen3 Max
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.6 Flash
Not ranked
Qwen3 Max
51.7
#78/120
Basis
Provisional lane · 0 vs 0 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Gemini 3.6 Flash
$0.00525
Fits in one request
Qwen3 Max
API rate not published
Fits in one request

Qwen3 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3.6 Flash
$0.0975
Fits in one request
Qwen3 Max
API rate not published
Fits in one request

Qwen3 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 3.6 Flash
$0.135
Fits in one request
Qwen3 Max
API rate not published
Fits in one request
Cached-input rate unavailable

Qwen3 Max 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.

Gemini 3.6 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

Qwen3 Max

No comparable hosted API rate

Reasoning profile

Gemini 3.6 Flash

Reasoning

Qwen3 Max

Reasoning

Weight access

Gemini 3.6 Flash

Proprietary

Qwen3 Max

Proprietary

License

Gemini 3.6 Flash

Proprietary

Qwen3 Max

Proprietary

Release date

Gemini 3.6 Flash

2026-07-21

Qwen3 Max

2026-04-20

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 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 evidence10 rows

Agentic

  • OSWorld-Verified

    Gemini 3.6 Flash83%
    Source
    Qwen3 Max

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.6 Flash73.8%
    Source
    Qwen3 Max

    Not directly comparable

  • Gert Labs

    Gemini 3.6 Flash
    Qwen3 Max43.74%
    Source

    Not directly comparable

Coding

  • deepSwe

    Gemini 3.6 Flash49%
    Source
    Qwen3 Max

    Not directly comparable

  • cursorBench32

    Gemini 3.6 Flash53.5%
    Source
    Qwen3 Max

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.6 Flash88.1%
    Source
    Qwen3 Max

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 3.6 Flash79.6%
    Source
    Qwen3 Max

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.6 Flash
    Qwen3 Max3.51%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3.6 Flash93.4%
    Source
    Qwen3 Max

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 3.6 Flash89.3%
    Source
    Qwen3 Max

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.6 Flash or Qwen3 Max?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3.6 Flash or Qwen3 Max?

Gemini 3.6 Flash scores higher for coding on the public lane, 58.9 to 45.4. Qwen3 Max 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, Gemini 3.6 Flash or Qwen3 Max?

Qwen3 Max is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 3.6 Flash or Qwen3 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, Gemini 3.6 Flash or Qwen3 Max?

Both models list the same context window, 1M.

Related comparisons

Last updated September 4, 2026

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