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BenchLM
Model A
DeepSeek V4 Flash 0731

DeepSeek

—

Evidence status unavailable

90% interval unavailable

DeepSeek V4 Flash 0731 vs MiniMax M3

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
MiniMax M3

MiniMax

68.4/100

Supported · Public rank #19

90% interval 63.4–73.4

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.

7 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    MiniMax M3

    MiniMax M3 leads on the same 2 weighted benchmark rows.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    DeepSeek V4 Flash 0731

    DeepSeek V4 Flash 0731 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 V4 Flash 0731

    DeepSeek V4 Flash 0731 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
7
DeepSeek V4 Flash 0731 only
26
MiniMax M3 only
15
Like-for-like categories
1 / 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

Like-for-like
DeepSeek V4 Flash 0731
68.8
MiniMax M3
72.2
Weighted basis
2 vs 2 rows
Reading
MiniMax M3 leads

Agentic

Directional only
DeepSeek V4 Flash 0731
63.8
MiniMax M3
72.3
Weighted basis
2 vs 3 rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Flash 0731
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Flash 0731
55.3
MiniMax M3
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Flash 0731
94.8
MiniMax M3
85.7
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Flash 0731
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Flash 0731
Not measured
MiniMax M3
64.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Flash 0731
Not measured
MiniMax M3
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.

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 V4 Flash 0731
$0.00028
Fits in one request
MiniMax M3
$0.0009
Fits in one request

DeepSeek V4 Flash 0731 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Flash 0731
$0.00784
Fits in one request
MiniMax M3
$0.0186
Fits in one request

DeepSeek V4 Flash 0731 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 V4 Flash 0731
$0.00616
Fits in one request
MiniMax M3
$0.03
Fits in one request

DeepSeek V4 Flash 0731 has the lower modeled cost

Costs use the listed standard API rates.

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 V4 Flash 0731

1M

DeepSeek models and pricing

MiniMax M3

1M

Cached-input rate

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

DeepSeek V4 Flash 0731

$0.0028 per 1M cached input tokens

MiniMax M3

$0.06 per 1M cached input tokens

Reasoning profile

DeepSeek V4 Flash 0731

Reasoning

MiniMax M3

Non-Reasoning

Weight access

DeepSeek V4 Flash 0731

Proprietary

MiniMax M3

Open Weight

License

DeepSeek V4 Flash 0731

Proprietary

MiniMax M3

Open Weight

Release date

DeepSeek V4 Flash 0731

2026-07-31

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
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: $0.00784 vs $0.0186. Cache-heavy agent loop: $0.00616 vs $0.03.
Context tradeoff
Both models list 1M.

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 evidence48 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    MiniMax M366%
    Source

    MiniMax M3 leads this result

  • BrowseComp

    DeepSeek V4 Flash 073173.2%
    Source
    MiniMax M383.5%
    Source

    MiniMax M3 leads this result

  • HLE w/ tools

    DeepSeek V4 Flash 073145.1%
    Source
    MiniMax M3—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Flash 073169%
    Source
    MiniMax M374.2%
    Source

    MiniMax M3 leads this result

  • Toolathlon

    DeepSeek V4 Flash 073147.8%
    Source
    MiniMax M3—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    MiniMax M3—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Flash 073176.7%
    Source
    MiniMax M3—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Flash 073170.3%
    Source
    MiniMax M3—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Flash 073125.2%
    Source
    MiniMax M3—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Flash 073125.1%
    Source
    MiniMax M3—

    Not directly comparable

  • OSWorld-Verified

    DeepSeek V4 Flash 0731—
    MiniMax M370.1%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Flash 0731—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    DeepSeek V4 Flash 0731—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek V4 Flash 0731—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    DeepSeek V4 Flash 0731—
    MiniMax M34.6%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Flash 073191.6%
    Source
    MiniMax M3—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Flash 07313052.0
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Flash 073179%
    Source
    MiniMax M380.5%
    Source

    MiniMax M3 leads this result

  • SWE-bench Pro

    DeepSeek V4 Flash 073152.6%
    Source
    MiniMax M359%
    Source

    MiniMax M3 leads this result

  • SWE Multilingual

    DeepSeek V4 Flash 073173.3%
    Source
    MiniMax M3—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Flash 073156.9%
    Source
    MiniMax M366.0%
    Source

    MiniMax M3 leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Flash 073182.7%
    Source
    MiniMax M3—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Flash 073154.2%
    Source
    MiniMax M342.1%
    Source

    DeepSeek V4 Flash 0731 leads this result

  • deepSwe

    DeepSeek V4 Flash 073154.4%
    Source
    MiniMax M3—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Flash 073168.7%
    Source
    MiniMax M3—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Flash 073159.6%
    Source
    MiniMax M3—

    Not directly comparable

  • VIBE V2

    DeepSeek V4 Flash 0731—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    DeepSeek V4 Flash 0731—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    DeepSeek V4 Flash 0731—
    MiniMax M328.8%
    Source

    Not directly comparable

  • EEBench

    DeepSeek V4 Flash 0731—
    MiniMax M39.2%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Flash 073178.7%
    Source
    MiniMax M3—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Flash 073160.5%
    Source
    MiniMax M3—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Flash 073186.2%
    Source
    MiniMax M3—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Flash 073134.1%
    Source
    MiniMax M3—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Flash 073178.9%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA

    DeepSeek V4 Flash 073188.1%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Flash 073188.1%
    Source
    MiniMax M3—

    Not directly comparable

  • HLE

    DeepSeek V4 Flash 073134.8%
    Source
    MiniMax M3—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Flash 073194.8%
    Source
    MiniMax M3—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Flash 073188.4%
    Source
    MiniMax M3—

    Not directly comparable

  • Apex

    DeepSeek V4 Flash 073133.0%
    Source
    MiniMax M3—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Flash 073185.7%
    Source
    MiniMax M3—

    Not directly comparable

  • USAMO 2026

    DeepSeek V4 Flash 0731—
    MiniMax M385.7%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    DeepSeek V4 Flash 0731—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    DeepSeek V4 Flash 0731—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek V4 Flash 0731—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    DeepSeek V4 Flash 0731—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    DeepSeek V4 Flash 0731—
    MiniMax M385.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Flash 0731 or MiniMax M3?

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, DeepSeek V4 Flash 0731 or MiniMax M3?

MiniMax M3 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.

Which is better for agentic tasks, DeepSeek V4 Flash 0731 or MiniMax M3?

The current agentic tasks 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 costs less, DeepSeek V4 Flash 0731 or MiniMax M3?

For the stated presets, chat costs $0.00028 on DeepSeek V4 Flash 0731 and $0.0009 on MiniMax M3; repository review costs $0.00784 and $0.0186; the cache-heavy agent loop costs $0.00616 and $0.03. Costs use the listed standard API rates.

Which has the larger context window, DeepSeek V4 Flash 0731 or MiniMax M3?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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