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BenchLM

Grok 4.7 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.

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

xAI logo
Model A
Grok 4.7

xAI

—

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.

  • Long documents

    Prompts that approach the documented context limit

    Grok 4.7

    Grok 4.7 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

    Grok 4.7 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

    Grok 4.7 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

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.

—Grok 4.748.8MiniMax M2.7

Not comparable · BenchAlign

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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
1
Grok 4.7 only
5
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
Grok 4.7
Not ranked
MiniMax M2.7
41.2
Estimated · #113/154
Basis
BenchAlign lane · 2 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
Grok 4.7
Not ranked
MiniMax M2.7
48.8
Estimated · #72/156
Basis
BenchAlign lane · 3 vs 11 public rows
Reading
Not comparable

Reasoning

Not comparable
Grok 4.7
73.7
Unranked · 2 rankable rows
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Grok 4.7
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Grok 4.7
Not ranked
MiniMax M2.7
49.1
Supported · #92/186
Basis
BenchAlign lane · 1 vs 4 public rows
Reading
Not comparable

Multilingual

Not comparable
Grok 4.7
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Grok 4.7
Not ranked
MiniMax M2.7
91.6
#10/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Grok 4.7
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.

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

Grok 4.7
$0.005
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

Grok 4.7
$0.118
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

Grok 4.7
$0.2
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.

Cached-input rate

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

Grok 4.7

$0.5 per 1M cached input tokens

xAI Grok 4.7 model documentation

MiniMax M2.7

Not published

Provider availability

Grok 4.7

Generally Available · xAI API, xAI US regional endpoint, Grok Build, Cursor, model gateways

xAI Grok 4.7 model documentation

MiniMax M2.7

Not sourced

Reasoning profile

Grok 4.7

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Grok 4.7

Proprietary

MiniMax M2.7

Open Weight

License

Grok 4.7

Proprietary

MiniMax M2.7

Open Weight

Release date

Grok 4.7

2026-09-21

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: $0.118 vs $0.0186. Cache-heavy agent loop: $0.2 vs $0.078.
Context tradeoff
Grok 4.7 has the larger documented window (500K).

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 evidence28 rows

Agentic

  • Terminal-Bench 4.0

    Grok 4.738.00%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Grok 4.776.0%
    Source
    MiniMax M2.748.7%
    Source

    Grok 4.7 leads this result

  • Terminal-Bench 2.0

    Grok 4.7—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Grok 4.7—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Grok 4.7—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Grok 4.7—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Grok 4.7—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Grok 4.7—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • cursorBench40

    Grok 4.746.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • DeepSWE

    Grok 4.771.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • EEBench

    Grok 4.764.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Grok 4.7—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Grok 4.7—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Grok 4.7—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Grok 4.7—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Grok 4.7—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Grok 4.7—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Grok 4.7—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Grok 4.7—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Grok 4.7—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Grok 4.7—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Grok 4.7—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Knowledge

  • HealthBench Professional

    Grok 4.756.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    Grok 4.7—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Grok 4.7—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Grok 4.7—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Grok 4.7—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Grok 4.7—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Questions

Which is better, Grok 4.7 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, Grok 4.7 or MiniMax M2.7?

Grok 4.7 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Grok 4.7 or MiniMax M2.7?

Grok 4.7 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Grok 4.7 or MiniMax M2.7?

For the stated presets, chat costs $0.005 on Grok 4.7 and $0.0009 on MiniMax M2.7; repository review costs $0.118 and $0.0186; the cache-heavy agent loop costs $0.2 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, Grok 4.7 or MiniMax M2.7?

Grok 4.7 has the larger documented context window: 500K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 21, 2026

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