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BenchLM
Thinking Machines Lab logo
Model A
Inkling

Thinking Machines Lab

67.02/100

Supported · Public rank #31

90% interval 60.0–74.0

Inkling vs MiniMax M2.7

Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

63.29/100

Supported · Public rank #49

90% interval 55.9–70.7

Decision reading

Inkling has the higher public score estimate, 67.02 versus 63.29, 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

    Inkling

    Inkling 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
Inkling only
12
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
Inkling
69.4
MiniMax M2.7
57.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Directional only
Inkling
68.6
MiniMax M2.7
53.3
Weighted basis
2 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
Inkling
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Inkling
51.6
MiniMax M2.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Inkling
97.1
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Inkling
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Inkling
76.5
MiniMax M2.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Inkling
79.8
MiniMax M2.7
Not measured
Weighted basis
1 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

Inkling
$0.00421
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

Inkling
$0.10754
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

Inkling
$0.159
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.

Inkling

1M

MiniMax M2.7

200K

API model ID

Inkling

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.

Inkling

$0.374 per 1M cached input tokens

MiniMax M2.7

Not published

Documented inputs

Inkling

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Inkling

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Inkling

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Inkling

Hybrid

MiniMax M2.7

Non-Reasoning

Weight access

Inkling

Open Weight

MiniMax M2.7

Open Weight

License

Inkling

Open Weight

MiniMax M2.7

Open Weight

Release date

Inkling

2026-07-15

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
Inkling has the higher public score estimate, 67.02 versus 63.29, but the 90% score intervals overlap.
Workload cost
Repository review: $0.10754 vs $0.0186. Cache-heavy agent loop: $0.159 vs $0.078.
Context tradeoff
Inkling 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 evidence30 rows

Agentic

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    MiniMax M2.757%
    Source

    Inkling leads this result

  • BrowseComp

    Inkling77.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MCP Atlas

    Inkling74.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon

    Inkling—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Inkling—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Inkling—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Inkling—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Inkling—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Inkling77.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Pro

    Inkling54.3%
    Source
    MiniMax M2.756.2%
    Source

    MiniMax M2.7 leads this result

  • Terminal-Bench 2.0

    Inkling63.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Inkling—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-Rebench

    Inkling—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Inkling—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Inkling—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Inkling—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Inkling—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Inkling—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Inkling—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Inkling87.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    Inkling87.9%
    Source
    MiniMax M2.787.0%
    Source

    Inkling leads this result

  • HLE

    Inkling46%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/o tools

    Inkling30%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Inkling—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • AIME26

    Inkling97.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    Inkling—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Inkling73.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CharXiv

    Inkling82%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CharXiv w/o tools

    Inkling78.1%
    Source
    MiniMax M2.7—

    Not directly comparable

Instruction following

  • IFBench

    Inkling79.8%
    Source
    MiniMax M2.7—

    Not directly comparable

Frequently asked questions

Which is better, Inkling or MiniMax M2.7?

Inkling has the higher public score estimate, 67.02 versus 63.29, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Inkling 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, Inkling 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, Inkling or MiniMax M2.7?

For the stated presets, chat costs $0.00421 on Inkling and $0.0009 on MiniMax M2.7; repository review costs $0.10754 and $0.0186; the cache-heavy agent loop costs $0.159 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, Inkling or MiniMax M2.7?

Inkling 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 29, 2026

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