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BenchLM

Composer 2 vs MiniMax M2.7

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.

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

Cursor logo
Model A
Composer 2

Cursor

—

Evidence status unavailable

90% interval unavailable

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.09/100

Supported · Public rank #100

90% interval 42.8–67.2

Updated September 21, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiniMax M2.7

    MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    MiniMax M2.7

    MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Composer 2 and MiniMax M2.7 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

    Composer 2 and MiniMax M2.7 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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. Composer 2 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

49.5Composer 248.8MiniMax M2.7

Directional only · BenchAlign

Composer 2 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.

Open the coding leaderboardShortlist models for this job

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
4
Composer 2 only
1
MiniMax M2.7 only
19
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.

Agentic

Directional only
Composer 2
49.5
Estimated · #64/154
MiniMax M2.7
41.2
Estimated · #113/154
Basis
BenchAlign lane · 1 vs 7 public rows
Reading
Directional only

Coding

Directional only
Composer 2
49.5
Estimated · #68/156
MiniMax M2.7
48.8
Estimated · #72/156
Basis
BenchAlign lane · 4 vs 11 public rows
Reading
Directional only

Reasoning

Not comparable
Composer 2
Not ranked
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Composer 2
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Composer 2
Not ranked
MiniMax M2.7
49.1
Supported · #92/186
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Not comparable

Multilingual

Not comparable
Composer 2
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Composer 2
Not ranked
MiniMax M2.7
91.6
#10/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Composer 2
Not ranked
MiniMax M2.7
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) 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.

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

Composer 2
$0.00175
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Composer 2
$0.0325
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Composer 2
$0.135
Does not fit in one request
Cached input priced at the published list-input rate
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

Composer 2 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input 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.

Composer 2

200K

MiniMax M2.7

200K

API model ID

Composer 2

Not sourced

MiniMax M2.7

Not sourced

Cached-input rate

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

Composer 2

Not published

MiniMax M2.7

Not published

Documented inputs

Composer 2

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Composer 2

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Composer 2

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Composer 2

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Composer 2

Proprietary

MiniMax M2.7

Open Weight

License

Composer 2

Proprietary

MiniMax M2.7

Open Weight

Release date

Composer 2

2026-03-19

MiniMax M2.7

2026-03-18

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
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
Repository review: $0.0325 vs $0.0186. Cache-heavy agent loop: $0.135 vs $0.078.
Context tradeoff
Both models list 200K.

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

    Composer 261.7%
    Source
    MiniMax M2.757%
    Source

    Composer 2 leads this result

  • Toolathlon

    Composer 2—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Composer 2—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Composer 2—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Composer 2—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Composer 2—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Composer 2—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE Multilingual

    Composer 273.7%
    Source
    MiniMax M2.776.5%
    Source

    MiniMax M2.7 leads this result

  • SWE-Rebench

    Shared source
    Composer 258%
    MiniMax M2.751.9%

    Composer 2 leads this result

  • React Native Evals

    Shared source
    Composer 296.1%
    MiniMax M2.771.4%

    Composer 2 leads this result

  • Terminal-Bench 2.0

    Composer 261.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Composer 2—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Composer 2—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Composer 2—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Composer 2—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Composer 2—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Composer 2—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Composer 2—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Composer 2—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Composer 2—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Composer 2—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Composer 2—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Composer 2—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Composer 2—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Questions

Which is better, Composer 2 or MiniMax M2.7?

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, Composer 2 or MiniMax M2.7?

Composer 2 scores higher for coding on the public lane, 49.5 to 48.8. Composer 2 and MiniMax M2.7 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, Composer 2 or MiniMax M2.7?

Composer 2 scores higher for agentic tasks on the public lane, 49.5 to 41.2. Composer 2 and MiniMax M2.7 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, Composer 2 or MiniMax M2.7?

For the stated presets, chat costs $0.00175 on Composer 2 and $0.0009 on MiniMax M2.7; repository review costs $0.0325 and $0.0186; the cache-heavy agent loop costs $0.135 and $0.078. Composer 2 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Composer 2 or MiniMax M2.7?

Both models list the same context window, 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 21, 2026

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