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Model comparison

Llama 4 Maverick vs MiniMax M2.7

Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

20 confirmed releases in the last 30 daysSee provider release alerts
Llama 4 Maverick

Meta

22.6/100

Supported · Public rank #203

90% interval 14.9–30.4

MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #40

90% interval 56.5–69.7

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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

    Llama 4 Maverick

    Llama 4 Maverick has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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. 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. Llama 4 Maverick 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
0
Llama 4 Maverick only
1
MiniMax M2.7 only
18
Like-for-like categories
0 / 8

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

Not comparable
Llama 4 Maverick
Not measured
MiniMax M2.7
57.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Llama 4 Maverick
Not measured
MiniMax M2.7
53.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Llama 4 Maverick
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Llama 4 Maverick
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Llama 4 Maverick
0.7
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Llama 4 Maverick
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Llama 4 Maverick
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Llama 4 Maverick
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.

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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Cache-heavy agent loop

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

Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits 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

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. Llama 4 Maverick 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.

Llama 4 Maverick

1M

MiniMax M2.7

200K

API model ID

Llama 4 Maverick

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.

Llama 4 Maverick

No comparable hosted API rate

MiniMax M2.7

Not published

Documented inputs

Llama 4 Maverick

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Llama 4 Maverick

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Llama 4 Maverick

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Llama 4 Maverick

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Llama 4 Maverick

Open Weight

MiniMax M2.7

Open Weight

License

Llama 4 Maverick

Open Weight

MiniMax M2.7

Open Weight

Release date

Llama 4 Maverick

2026-02-28

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Llama 4 Maverick has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Llama 4 Maverick
API / mo$0
Self-host / mo$2,610
Break-even—
MiniMax M2.7
API / mo$1,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence19 rows

Agentic

  • Terminal-Bench 2.0

    Llama 4 Maverick—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Llama 4 Maverick—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Llama 4 Maverick—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Llama 4 Maverick—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Llama 4 Maverick—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Llama 4 Maverick—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified*

    Llama 4 Maverick—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Llama 4 Maverick—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Llama 4 Maverick—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Llama 4 Maverick—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Llama 4 Maverick—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Llama 4 Maverick—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Llama 4 Maverick—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Llama 4 Maverick—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Llama 4 Maverick—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Llama 4 Maverick—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Llama 4 Maverick—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Llama 4 Maverick0.690%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    Llama 4 Maverick—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Llama 4 Maverick or MiniMax M2.7?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Llama 4 Maverick or MiniMax M2.7?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Llama 4 Maverick or MiniMax M2.7?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Llama 4 Maverick 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, Llama 4 Maverick or MiniMax M2.7?

Llama 4 Maverick 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 1, 2026

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