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BenchLM
DeepSeek logo
Model A
DeepSeek V3

DeepSeek

44.57/100

Supported · Public rank #174

90% interval 25.5–63.7

DeepSeek V3 vs MiniMax M3

Updated August 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M3

MiniMax

68.73/100

Supported · Public rank #21

90% interval 63.5–74.0

Decision reading

MiniMax M3 has the higher public score estimate, 68.73 versus 44.57, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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

    MiniMax M3

    MiniMax M3 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V3

    DeepSeek V3 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

    DeepSeek V3

    DeepSeek V3 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

    No shared weighted benchmark basis supports a winner.

    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. DeepSeek V3 does not fit this workload in one request.

    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
1
DeepSeek V3 only
5
MiniMax M3 only
21
Like-for-like categories
0 / 8

1 category uses 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.

Coding

Directional only
DeepSeek V3
38.9
MiniMax M3
72.2
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V3
Not measured
MiniMax M3
72.3
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V3
72.7
MiniMax M3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3
1.7
MiniMax M3
85.7
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3
Not measured
MiniMax M3
64.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3
86.1
MiniMax M3
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

DeepSeek V3
$0.00082
Fits in one request
MiniMax M3
$0.0009
Fits in one request

DeepSeek V3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3
$0.0168
Fits in one request
MiniMax M3
$0.0186
Fits in one request

DeepSeek V3 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

DeepSeek V3
$0.0304
Does not fit in one request
MiniMax M3
$0.03
Fits in one request

DeepSeek V3 does not fit this workload in one request.

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.

DeepSeek V3

128K

MiniMax M3

1M

API model ID

DeepSeek V3

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.

DeepSeek V3

$0.07 per 1M cached input tokens

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

DeepSeek V3

Not sourced

MiniMax M3

Not sourced

Documented outputs

DeepSeek V3

Not sourced

MiniMax M3

Not sourced

Provider availability

DeepSeek V3

Not sourced

MiniMax M3

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

MiniMax M3

Non-Reasoning

Weight access

DeepSeek V3

Open Weight

MiniMax M3

Open Weight

License

DeepSeek V3

Open Weight

MiniMax M3

Open Weight

Release date

DeepSeek V3

2024-12-26

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
MiniMax M3 has the higher public score estimate, 68.73 versus 44.57, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0168 vs $0.0186. Cache-heavy agent loop: $0.0304 vs $0.03.
Context tradeoff
MiniMax M3 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.

DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
MiniMax M3
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 evidence27 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V3—
    MiniMax M366%
    Source

    Not directly comparable

  • BrowseComp

    DeepSeek V3—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    DeepSeek V3—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek V3—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V3—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    DeepSeek V3—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek V3—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    DeepSeek V3—
    MiniMax M34.6%
    Source

    Not directly comparable

Coding

  • LiveCodeBench

    DeepSeek V337.6%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V342%
    Source
    MiniMax M380.5%
    Source

    MiniMax M3 leads this result

  • SWE-bench Pro

    DeepSeek V3—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V3—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek V3—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    DeepSeek V3—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    DeepSeek V3—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    DeepSeek V3—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V3—
    MiniMax M348.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    DeepSeek V359.1%
    Source
    MiniMax M3—

    Not directly comparable

  • MMLU-Pro

    DeepSeek V375.9%
    Source
    MiniMax M3—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V31.724%
    Source
    MiniMax M3—

    Not directly comparable

  • USAMO 2026

    DeepSeek V3—
    MiniMax M385.7%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    DeepSeek V3—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    DeepSeek V3—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek V3—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    DeepSeek V3—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    DeepSeek V3—
    MiniMax M385.4%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek V386.1%
    Source
    MiniMax M3—

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3 or MiniMax M3?

MiniMax M3 has the higher public score estimate, 68.73 versus 44.57, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V3 or MiniMax M3?

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, DeepSeek V3 or MiniMax M3?

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, DeepSeek V3 or MiniMax M3?

For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.0009 on MiniMax M3; repository review costs $0.0168 and $0.0186; the cache-heavy agent loop costs $0.0304 and $0.03. DeepSeek V3 does not fit this workload in one request.

Which has the larger context window, DeepSeek V3 or MiniMax M3?

MiniMax M3 has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 30, 2026

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