Skip to main content
Radar

Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.

See Radar
BenchLM
Advertise

Model comparison

MiniMax M2.7 vs Qwen 3.6 Max (preview)

Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax M2.7

MiniMax

63.0/100

Supported · Public rank #44

90% interval 56.2–69.8

Qwen 3.6 Max (preview)

Alibaba

59.6/100

Supported · Public rank #56

90% interval 40.8–78.5

MiniMax M2.7 has the higher public score estimate, 62.98 versus 59.65, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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.

  • Agentic work

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

    Qwen 3.6 Max (preview)

    Qwen 3.6 Max (preview) leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Qwen 3.6 Max (preview)

    Qwen 3.6 Max (preview) 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

  • 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 M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Qwen 3.6 Max (preview) 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
MiniMax M2.7 only
15
Qwen 3.6 Max (preview) only
6
Like-for-like categories
1 / 8

1 category uses 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.

Agentic

Like-for-like
MiniMax M2.7
57.0
Qwen 3.6 Max (preview)
65.4
Weighted basis
1 vs 1 rows
Reading
Qwen 3.6 Max (preview) leads

Coding

Directional only
MiniMax M2.7
53.3
Qwen 3.6 Max (preview)
51.0
Weighted basis
2 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
MiniMax M2.7
Not measured
Qwen 3.6 Max (preview)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
MiniMax M2.7
Not measured
Qwen 3.6 Max (preview)
73.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
MiniMax M2.7
Not measured
Qwen 3.6 Max (preview)
18.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
MiniMax M2.7
Not measured
Qwen 3.6 Max (preview)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
MiniMax M2.7
Not measured
Qwen 3.6 Max (preview)
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
MiniMax M2.7
Not measured
Qwen 3.6 Max (preview)
Not measured
Weighted basis
0 vs 0 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

MiniMax M2.7
$0.0009
Fits in one request
Qwen 3.6 Max (preview)
API rate not published
Fits in one request

Qwen 3.6 Max (preview) has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiniMax M2.7
$0.0186
Fits in one request
Qwen 3.6 Max (preview)
API rate not published
Fits in one request

Qwen 3.6 Max (preview) has no comparable published API token rate.

Cache-heavy agent loop

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

MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate
Qwen 3.6 Max (preview)
API rate not published
Fits in one request
Cached-input rate unavailable

MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Qwen 3.6 Max (preview) 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.

MiniMax M2.7

200K

Qwen 3.6 Max (preview)

256K

API model ID

MiniMax M2.7

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Cached-input rate

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

MiniMax M2.7

Not published

Qwen 3.6 Max (preview)

No comparable hosted API rate

Documented inputs

MiniMax M2.7

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Documented outputs

MiniMax M2.7

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Provider availability

MiniMax M2.7

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Reasoning profile

MiniMax M2.7

Non-Reasoning

Qwen 3.6 Max (preview)

Reasoning

Weight access

MiniMax M2.7

Open Weight

Qwen 3.6 Max (preview)

Proprietary

License

MiniMax M2.7

Open Weight

Qwen 3.6 Max (preview)

Proprietary

Release date

MiniMax M2.7

2026-03-18

Qwen 3.6 Max (preview)

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
MiniMax M2.7 has the higher public score estimate, 62.98 versus 59.65, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen 3.6 Max (preview) has the larger documented window (256K).

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 evidence24 rows

Agentic

  • Terminal-Bench 2.0

    MiniMax M2.757%
    Source
    Qwen 3.6 Max (preview)65.4%
    Source

    Qwen 3.6 Max (preview) leads this result

  • Toolathlon

    MiniMax M2.746.3%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • MLE-Bench Lite

    MiniMax M2.766.6%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • MM-ClawBench

    MiniMax M2.762.7%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • Claw-Eval

    MiniMax M2.748.7%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • Gert Labs

    MiniMax M2.740.40%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • QwenClawBench

    MiniMax M2.7—
    Qwen 3.6 Max (preview)59.0%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified*

    MiniMax M2.775.4%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • SWE-bench Pro

    MiniMax M2.756.2%
    Source
    Qwen 3.6 Max (preview)57.3%
    Source

    Qwen 3.6 Max (preview) leads this result

  • SWE-Rebench

    MiniMax M2.751.9%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • SWE Multilingual

    MiniMax M2.776.5%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • Multi-SWE Bench

    MiniMax M2.752.7%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • VIBE-Pro

    MiniMax M2.755.6%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • NL2Repo

    MiniMax M2.739.8%
    Source
    Qwen 3.6 Max (preview)42.9%
    Source

    Qwen 3.6 Max (preview) leads this result

  • Vibe Code Bench

    MiniMax M2.727.04%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • React Native Evals

    MiniMax M2.771.4%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • SciCode

    MiniMax M2.7—
    Qwen 3.6 Max (preview)47%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    MiniMax M2.7—
    Qwen 3.6 Max (preview)65.4%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    MiniMax M2.787.0%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • MMLU-Pro (Arcee)

    MiniMax M2.780.8%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • SuperGPQA

    MiniMax M2.7—
    Qwen 3.6 Max (preview)73.9%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    MiniMax M2.780.0%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    MiniMax M2.7—
    Qwen 3.6 Max (preview)23.103%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    MiniMax M2.7—
    Qwen 3.6 Max (preview)4.167%
    Source

    Not directly comparable

Frequently asked questions

Which is better, MiniMax M2.7 or Qwen 3.6 Max (preview)?

MiniMax M2.7 has the higher public score estimate, 62.98 versus 59.65, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, MiniMax M2.7 or Qwen 3.6 Max (preview)?

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, MiniMax M2.7 or Qwen 3.6 Max (preview)?

Qwen 3.6 Max (preview) leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, MiniMax M2.7 or Qwen 3.6 Max (preview)?

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 M2.7 or Qwen 3.6 Max (preview)?

Qwen 3.6 Max (preview) has the larger documented context window: 256K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 11, 2026

Watch MiniMax M2.7 vs Qwen 3.6 Max (preview)

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.