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BenchLM

MiniMax M2.5 vs Qwen3.5-35B-A3B

Updated September 28, 2026. Rank says MiniMax M2.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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.

Model A
MiniMax logo

MiniMax

49.85/100

Supported · Public rank #79

90% interval 40.3–59.5

Model B
Alibaba logo

Alibaba

47/100

Supported · Public rank #89

90% interval 36.7–57.3

Shared results
0
MiniMax M2.5 only
1
Qwen3.5-35B-A3B only
16
Like-for-like categories
0 / 8
Supported: MiniMax M2.5 and Qwen3.5-35B-A3BHow 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.

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.5-35B-A3B

    Qwen3.5-35B-A3B 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

    MiniMax M2.5 and Qwen3.5-35B-A3B are 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

    MiniMax M2.5 and Qwen3.5-35B-A3B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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. MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5-35B-A3B 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

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.

35.7MiniMax M2.526.6Qwen3.5-35B-A3B

Directional only · BenchAlign v5.7

MiniMax M2.5 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

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.

2 categories rest 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.

A shared-evidence shape is not available.

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Agentic

Directional only
MiniMax M2.5
28.6
Estimated · #78/111
Qwen3.5-35B-A3B
29.1
Estimated · #76/111
Basis
BenchAlign v5.7 lane · 0 vs 4 public rows
Reading
Directional only

Coding

Directional only
MiniMax M2.5
35.7
Estimated · #72/136
Qwen3.5-35B-A3B
26.6
Estimated · #96/136
Basis
BenchAlign v5.7 lane · 1 vs 2 public rows
Reading
Directional only

Reasoning

Not comparable
MiniMax M2.5
Not ranked
Qwen3.5-35B-A3B
41.4
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
MiniMax M2.5
Not ranked
Qwen3.5-35B-A3B
68.2
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
MiniMax M2.5
Not ranked
Qwen3.5-35B-A3B
39.7
Supported · #94/160
Basis
BenchAlign v5.7 lane · 0 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
MiniMax M2.5
Not ranked
Qwen3.5-35B-A3B
21.1
#11/12
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
MiniMax M2.5
Not ranked
Qwen3.5-35B-A3B
87.4
#30/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
MiniMax M2.5
Not ranked
Qwen3.5-35B-A3B
Not ranked
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 v5.7) 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

MiniMax M2.5
$0.0009
Fits in one request
Qwen3.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.5-35B-A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiniMax M2.5
$0.0186
Fits in one request
Qwen3.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.5-35B-A3B has no comparable published API token rate.

Cache-heavy agent loop

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

MiniMax M2.5
$0.078
Does not fit in one request
Cached input priced at the published list-input rate
Qwen3.5-35B-A3B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

MiniMax M2.5 does not fit this workload in one request. MiniMax M2.5 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5-35B-A3B 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.

Context window

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

MiniMax M2.5

128K

Qwen3.5-35B-A3B

262K

API model ID

MiniMax M2.5

Not sourced

Qwen3.5-35B-A3B

Not sourced

Cached-input rate

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

MiniMax M2.5

Not published

Qwen3.5-35B-A3B

No comparable hosted API rate

Documented inputs

MiniMax M2.5

Not sourced

Qwen3.5-35B-A3B

Not sourced

Documented outputs

MiniMax M2.5

Not sourced

Qwen3.5-35B-A3B

Not sourced

Provider availability

MiniMax M2.5

Not sourced

Qwen3.5-35B-A3B

Not sourced

Reasoning profile

MiniMax M2.5

Non-Reasoning

Qwen3.5-35B-A3B

Reasoning

Weight access

MiniMax M2.5

Proprietary

Qwen3.5-35B-A3B

Open Weight

License

MiniMax M2.5

Proprietary

Qwen3.5-35B-A3B

Open Weight

Release date

MiniMax M2.5

2025-10-01

Qwen3.5-35B-A3B

2026-03-04

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
Qwen3.5-35B-A3B has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, MiniMax M2.5 or Qwen3.5-35B-A3B?

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, MiniMax M2.5 or Qwen3.5-35B-A3B?

MiniMax M2.5 scores higher for coding on the public lane, 35.7 to 26.6. MiniMax M2.5 and Qwen3.5-35B-A3B are 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, MiniMax M2.5 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B scores higher for agentic tasks on the public lane, 29.1 to 28.6. MiniMax M2.5 and Qwen3.5-35B-A3B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, MiniMax M2.5 or Qwen3.5-35B-A3B?

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, MiniMax M2.5 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B has the larger documented context window: 262K, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence17 rows

Agentic

  • Terminal-Bench 2.0

    MiniMax M2.5—
    Qwen3.5-35B-A3B40.5%
    Source

    Not directly comparable

  • BrowseComp

    MiniMax M2.5—
    Qwen3.5-35B-A3B61%
    Source

    Not directly comparable

  • OSWorld-Verified

    MiniMax M2.5—
    Qwen3.5-35B-A3B54.5%
    Source

    Not directly comparable

  • Gert Labs

    MiniMax M2.5—
    Qwen3.5-35B-A3B28.96%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    MiniMax M2.514.85%
    Source
    Qwen3.5-35B-A3B—

    Not directly comparable

  • SWE-bench Verified

    MiniMax M2.5—
    Qwen3.5-35B-A3B69.2%
    Source

    Not directly comparable

  • SWE-Rebench

    MiniMax M2.5—
    Qwen3.5-35B-A3B53.7%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    MiniMax M2.5—
    Qwen3.5-35B-A3B59%
    Source

    Not directly comparable

Multimodal

  • MMMU

    MiniMax M2.5—
    Qwen3.5-35B-A3B81.4%
    Source

    Not directly comparable

  • MMVU

    MiniMax M2.5—
    Qwen3.5-35B-A3B72.3%
    Source

    Not directly comparable

  • MathVision

    MiniMax M2.5—
    Qwen3.5-35B-A3B83.9%
    Source

    Not directly comparable

  • V*

    MiniMax M2.5—
    Qwen3.5-35B-A3B92.7%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    MiniMax M2.5—
    Qwen3.5-35B-A3B85.3%
    Source

    Not directly comparable

  • SuperGPQA

    MiniMax M2.5—
    Qwen3.5-35B-A3B63.4%
    Source

    Not directly comparable

  • GPQA

    MiniMax M2.5—
    Qwen3.5-35B-A3B84.2%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    MiniMax M2.5—
    Qwen3.5-35B-A3B81%
    Source

    Not directly comparable

Instruction following

  • IFEval

    MiniMax M2.5—
    Qwen3.5-35B-A3B91.9%
    Source

    Not directly comparable

17 public results · 0 shared

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Last updated September 28, 2026