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BenchLM

LongCat-2.0 vs MiniMax M3

Updated September 29, 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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Decision reading

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Meituan logo

Meituan

—

Evidence status unavailable

90% interval unavailable

Model B
MiniMax logo

MiniMax

54.8/100

Supported · Public rank #63

90% interval 46.2–63.4

Shared results
0
LongCat-2.0 only
0
MiniMax M3 only
27
Like-for-like categories
0 / 8
Supported: MiniMax M3How the comparison works

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

    LongCat-2.0 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

    LongCat-2.0 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

    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

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.

—LongCat-2.038.8MiniMax M3

Not comparable · BenchAlign v5.7

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.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
LongCat-2.0
Not ranked
MiniMax M3
39.7
Supported · #56/117
Basis
BenchAlign v5.7 lane · 0 vs 9 public rows
Reading
Not comparable

Coding

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
38.8
Supported · #65/142
Basis
BenchAlign v5.7 lane · 0 vs 10 public rows
Reading
Not comparable

Reasoning

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
79.4
#5/27
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
52.0
#37/50
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Knowledge

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
48.3
Supported · #67/168
Basis
BenchAlign v5.7 lane · 0 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
92.4
#5/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
LongCat-2.0
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

LongCat-2.0
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M3
$0.0009
Fits in one request

LongCat-2.0 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LongCat-2.0
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M3
$0.0186
Fits in one request

LongCat-2.0 has no comparable published API token rate.

Cache-heavy agent loop

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

LongCat-2.0
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
MiniMax M3
$0.03
Fits in one request

LongCat-2.0 has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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.

LongCat-2.0

1M

MiniMax M3

1M

API model ID

LongCat-2.0

Not sourced

MiniMax M3

Not sourced

Cached-input rate

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

LongCat-2.0

No comparable hosted API rate

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

LongCat-2.0

Not sourced

MiniMax M3

Not sourced

Documented outputs

LongCat-2.0

Not sourced

MiniMax M3

Not sourced

Provider availability

LongCat-2.0

Not sourced

MiniMax M3

Not sourced

Reasoning profile

LongCat-2.0

Reasoning

MiniMax M3

Non-Reasoning

Weight access

LongCat-2.0

Open Weight

MiniMax M3

Open Weight

License

LongCat-2.0

Open Weight

MiniMax M3

Open Weight

Release date

LongCat-2.0

2026-06-30

MiniMax M3

2026-06-01

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
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, LongCat-2.0 or MiniMax M3?

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, LongCat-2.0 or MiniMax M3?

LongCat-2.0 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LongCat-2.0 or MiniMax M3?

LongCat-2.0 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LongCat-2.0 or MiniMax M3?

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, LongCat-2.0 or MiniMax M3?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence27 rows

Agentic

  • Terminal-Bench 2.1

    LongCat-2.0—
    MiniMax M366.0%
    Source

    Not directly comparable

  • BrowseComp

    LongCat-2.0—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    LongCat-2.0—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    LongCat-2.0—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    LongCat-2.0—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    LongCat-2.0—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    LongCat-2.0—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    LongCat-2.0—
    MiniMax M34.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    LongCat-2.0—
    MiniMax M353.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    LongCat-2.0—
    MiniMax M380.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    LongCat-2.0—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    LongCat-2.0—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    LongCat-2.0—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    LongCat-2.0—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    LongCat-2.0—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    LongCat-2.0—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    LongCat-2.0—
    MiniMax M348.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    LongCat-2.0—
    MiniMax M382.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    LongCat-2.0—
    MiniMax M375.0%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    LongCat-2.0—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    LongCat-2.0—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    LongCat-2.0—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    LongCat-2.0—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    LongCat-2.0—
    MiniMax M385.4%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    LongCat-2.0—
    MiniMax M392.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    LongCat-2.0—
    MiniMax M384.2%
    Source

    Not directly comparable

Math

  • USAMO 2026

    LongCat-2.0—
    MiniMax M385.7%
    Source

    Not directly comparable

27 public results · 0 shared

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Last updated September 29, 2026