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Model A
Claude Sonnet 4.5 Thinking

Anthropic

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Evidence status unavailable

90% interval unavailable

Claude Sonnet 4.5 Thinking vs MiniMax M2.7

Updated September 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.14/100

Supported · Public rank #95

90% interval 43.8–66.5

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.

1 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

    Claude Sonnet 4.5 Thinking is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Claude Sonnet 4.5 Thinking 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.5 Thinking does not fit this 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. Claude Sonnet 4.5 Thinking 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
1
Claude Sonnet 4.5 Thinking only
0
MiniMax M2.7 only
22
Like-for-like categories
0 / 8

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

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
41.1
Estimated · #110/153
Basis
BenchAlign lane · 0 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
48.6
Estimated · #68/152
Basis
BenchAlign lane · 1 vs 11 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
48.7
Supported · #90/183
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 4.5 Thinking
Not ranked
MiniMax M2.7
93.0
#10/123
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

Claude Sonnet 4.5 Thinking
API rate not published
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

Claude Sonnet 4.5 Thinking has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 4.5 Thinking
API rate not published
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

Claude Sonnet 4.5 Thinking has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Sonnet 4.5 Thinking
API rate not published
Does not fit in one request
Cached-input rate unavailable
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

Claude Sonnet 4.5 Thinking does not fit this 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. Claude Sonnet 4.5 Thinking 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.

Claude Sonnet 4.5 Thinking

200K

MiniMax M2.7

200K

API model ID

Claude Sonnet 4.5 Thinking

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.

Claude Sonnet 4.5 Thinking

No comparable hosted API rate

MiniMax M2.7

Not published

Documented inputs

Claude Sonnet 4.5 Thinking

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Claude Sonnet 4.5 Thinking

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Claude Sonnet 4.5 Thinking

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Claude Sonnet 4.5 Thinking

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Claude Sonnet 4.5 Thinking

Proprietary

MiniMax M2.7

Open Weight

License

Claude Sonnet 4.5 Thinking

Proprietary

MiniMax M2.7

Open Weight

Release date

Claude Sonnet 4.5 Thinking

2025-09-01

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
A complete comparable API-rate estimate is not available for both models.
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 evidence23 rows

Agentic

  • Terminal-Bench 2.0

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    Claude Sonnet 4.5 Thinking22.62%
    MiniMax M2.727.04%

    MiniMax M2.7 leads this result

  • SWE-bench Verified*

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • React Native Evals

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Claude Sonnet 4.5 Thinking—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 4.5 Thinking 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, Claude Sonnet 4.5 Thinking or MiniMax M2.7?

Claude Sonnet 4.5 Thinking is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Sonnet 4.5 Thinking or MiniMax M2.7?

Claude Sonnet 4.5 Thinking is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Sonnet 4.5 Thinking or MiniMax M2.7?

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, Claude Sonnet 4.5 Thinking 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 15, 2026

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