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Model comparison

MiniMax M3 vs NExT-GPT 7B

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax M3

MiniMax

68.8/100

Supported · Public rank #18

90% interval 64.3–73.3

NExT-GPT 7B

NExT-GPT authors

—

Evidence status unavailable

90% interval unavailable

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.

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

    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

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    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

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

    Confidence: listed-rates

  • 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
MiniMax M3 only
21
NExT-GPT 7B only
0
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
MiniMax M3
72.3
NExT-GPT 7B
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Coding

Not comparable
MiniMax M3
72.2
NExT-GPT 7B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
MiniMax M3
Not measured
NExT-GPT 7B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
MiniMax M3
Not measured
NExT-GPT 7B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
MiniMax M3
85.7
NExT-GPT 7B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
MiniMax M3
Not measured
NExT-GPT 7B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
MiniMax M3
64.9
NExT-GPT 7B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
MiniMax M3
Not measured
NExT-GPT 7B
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

MiniMax M3
$0.0009
Fits in one request
NExT-GPT 7B
API rate not published
Fit state unavailable

NExT-GPT 7B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

MiniMax M3
$0.0186
Fits in one request
NExT-GPT 7B
API rate not published
Fit state unavailable

NExT-GPT 7B has no comparable published API token rate.

Cache-heavy agent loop

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

MiniMax M3
$0.03
Fits in one request
NExT-GPT 7B
API rate not published
Fit state unavailable
Cached-input rate unavailable

NExT-GPT 7B 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.

MiniMax M3

1M

NExT-GPT 7B

N/A

API model ID

MiniMax M3

Not sourced

NExT-GPT 7B

Not sourced

Cached-input rate

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

MiniMax M3

$0.06 per 1M cached input tokens

NExT-GPT 7B

No comparable hosted API rate

NExT-GPT authors model documentation

Documented inputs

MiniMax M3

Not sourced

NExT-GPT 7B

Not sourced

Documented outputs

MiniMax M3

Not sourced

NExT-GPT 7B

Not sourced

Provider availability

MiniMax M3

Not sourced

NExT-GPT 7B

Not sourced

Reasoning profile

MiniMax M3

Non-Reasoning

NExT-GPT 7B

Non-Reasoning

Weight access

MiniMax M3

Open Weight

NExT-GPT 7B

Open Weight

License

MiniMax M3

Open Weight

NExT-GPT 7B

Open Weight

Release date

MiniMax M3

2026-06-01

NExT-GPT 7B

Not sourced

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
A complete documented context comparison is not available.

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 evidence21 rows

Agentic

  • Terminal-Bench 2.0

    MiniMax M366%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • BrowseComp

    MiniMax M383.5%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • OSWorld-Verified

    MiniMax M370.1%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • MCP Atlas

    MiniMax M374.2%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • Claw-Eval

    MiniMax M374.5%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • BankerToolBench

    MiniMax M376.1%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • ResearchClawBench

    MiniMax M319.8%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • OSWorld 2.0

    MiniMax M34.6%
    Source
    NExT-GPT 7B—

    Not directly comparable

Coding

  • SWE-bench Verified

    MiniMax M380.5%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • SWE-bench Pro

    MiniMax M359%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • Terminal-Bench 2.0

    MiniMax M366.0%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • NL2Repo

    MiniMax M342.1%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • VIBE V2

    MiniMax M350.1%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • SVG-Bench

    MiniMax M363.7%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • KernelBench Hard

    MiniMax M328.8%
    Source
    NExT-GPT 7B—

    Not directly comparable

Math

  • USAMO 2026

    MiniMax M385.7%
    Source
    NExT-GPT 7B—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    MiniMax M345.1%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • OmniDocBench 1.5

    MiniMax M391.6%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • MMMU-Pro

    MiniMax M378.1%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • VideoMMMU

    MiniMax M384.6%
    Source
    NExT-GPT 7B—

    Not directly comparable

  • Video-MME (with subtitle)

    MiniMax M385.4%
    Source
    NExT-GPT 7B—

    Not directly comparable

Frequently asked questions

Which is better, MiniMax M3 or NExT-GPT 7B?

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, MiniMax M3 or NExT-GPT 7B?

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, MiniMax M3 or NExT-GPT 7B?

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, MiniMax M3 or NExT-GPT 7B?

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, MiniMax M3 or NExT-GPT 7B?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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