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BenchLM
Model A
Claude Sonnet 5

Anthropic

64.8/100

Estimated · Public rank #37

90% interval 50.5–79.1

Claude Sonnet 5 vs MiniMax M2.7

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #46

90% interval 56.0–70.1

Decision reading

Claude Sonnet 5 has the higher public score estimate, 64.78 versus 63.06, 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.

  • Long documents

    Prompts that approach the documented context limit

    Claude Sonnet 5

    Claude Sonnet 5 has the larger documented context window.

    Confidence: documented

  • 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • 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.

    Confidence: rate-fallback

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
Claude Sonnet 5 only
16
MiniMax M2.7 only
15
Like-for-like categories
0 / 8

2 categories use 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

Directional only
Claude Sonnet 5
81.9
MiniMax M2.7
57.0
Weighted basis
3 vs 1 rows
Reading
Directional only

Coding

Directional only
Claude Sonnet 5
76.7
MiniMax M2.7
53.3
Weighted basis
2 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
Claude Sonnet 5
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Sonnet 5
57.4
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 5
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 5
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 5
88.3
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 5
Not measured
MiniMax M2.7
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

Claude Sonnet 5
$0.007
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

Claude Sonnet 5
$0.13
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

Claude Sonnet 5
$0.18
Fits in one request
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

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.

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.

Claude Sonnet 5

1M

Anthropic model overview

MiniMax M2.7

200K

Cached-input rate

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

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

MiniMax M2.7

Not published

Reasoning profile

Claude Sonnet 5

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Claude Sonnet 5

Proprietary

MiniMax M2.7

Open Weight

License

Claude Sonnet 5

Proprietary

MiniMax M2.7

Open Weight

Release date

Claude Sonnet 5

2026-06-30

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
Claude Sonnet 5 has the higher public score estimate, 64.78 versus 63.06, but the 90% score intervals overlap.
Workload cost
Repository review: $0.13 vs $0.0186. Cache-heavy agent loop: $0.18 vs $0.078.
Context tradeoff
Claude Sonnet 5 has the larger documented window (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 evidence34 rows

Agentic

  • Terminal-Bench 3.0

    Claude Sonnet 514.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Sonnet 580.4%
    Source
    MiniMax M2.757%
    Source

    Claude Sonnet 5 leads this result

  • BrowseComp

    Claude Sonnet 584.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/ tools

    Claude Sonnet 557.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 581.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon

    Claude Sonnet 5—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Claude Sonnet 5—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Claude Sonnet 5—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Sonnet 5—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Claude Sonnet 5—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 585.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Pro

    Claude Sonnet 563.2%
    Source
    MiniMax M2.756.2%
    Source

    Claude Sonnet 5 leads this result

  • SWE Multilingual

    Claude Sonnet 578.3%
    Source
    MiniMax M2.776.5%
    Source

    Claude Sonnet 5 leads this result

  • SWE Multimodal

    Claude Sonnet 528.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Sonnet 580.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Sonnet 542.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • cursorBench32

    Claude Sonnet 561.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • APEX-SWE

    Claude Sonnet 546.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • EEBench

    Claude Sonnet 540.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • 3DCodeBench

    Claude Sonnet 539.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Claude Sonnet 5—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-Rebench

    Claude Sonnet 5—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Claude Sonnet 5—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Claude Sonnet 5—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Claude Sonnet 5—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude Sonnet 5—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Claude Sonnet 5—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • HLE

    Claude Sonnet 557.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/o tools

    Claude Sonnet 543.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    Claude Sonnet 5—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Sonnet 5—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Claude Sonnet 5—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Claude Sonnet 588.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CharXiv w/o tools

    Claude Sonnet 577%
    Source
    MiniMax M2.7—

    Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 5 or MiniMax M2.7?

Claude Sonnet 5 has the higher public score estimate, 64.78 versus 63.06, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Sonnet 5 or MiniMax M2.7?

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, Claude Sonnet 5 or MiniMax M2.7?

The current agentic tasks 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 costs less, Claude Sonnet 5 or MiniMax M2.7?

For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.0009 on MiniMax M2.7; repository review costs $0.13 and $0.0186; the cache-heavy agent loop costs $0.18 and $0.078. 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.

Which has the larger context window, Claude Sonnet 5 or MiniMax M2.7?

Claude Sonnet 5 has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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