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BenchLM
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Model A
DeepSeek V4 Pro 0813

DeepSeek

66.39/100

Estimated · Public rank #34

90% interval 54.9–77.9

DeepSeek V4 Pro 0813 vs MiniMax M2.7

Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.26/100

Supported · Public rank #96

90% interval 43.6–66.8

Decision reading

DeepSeek V4 Pro 0813 has the higher public score estimate, 66.39 versus 55.26, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

12 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Pro 0813

    DeepSeek V4 Pro 0813 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

    MiniMax M2.7

    MiniMax M2.7 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

    MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    MiniMax M2.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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. 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.

    Confidence: rate-fallback

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
12
DeepSeek V4 Pro 0813 only
28
MiniMax M2.7 only
11
Like-for-like categories
0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Directional only
DeepSeek V4 Pro 0813
56.5
Supported · #34/153
MiniMax M2.7
41.3
Estimated · #111/153
Basis
BenchAlign lane · 11 vs 7 public rows
Reading
Directional only

Coding

Directional only
DeepSeek V4 Pro 0813
51.9
Supported · #49/152
MiniMax M2.7
48.8
Estimated · #68/152
Basis
BenchAlign lane · 15 vs 11 public rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
59.8
Estimated · #38/183
MiniMax M2.7
48.7
Supported · #90/183
Basis
BenchAlign lane · 8 vs 4 public rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro 0813
60.3
#15/20
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
80.4
Unranked · 4 rankable rows
MiniMax M2.7
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not ranked
MiniMax M2.7
93.0
#10/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

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

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 Pro 0813
$0.00087
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

DeepSeek V4 Pro 0813 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro 0813
$0.02436
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

MiniMax M2.7 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 Pro 0813
$0.01812
Fits in one request
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.

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 Pro 0813

1M

DeepSeek models and pricing

MiniMax M2.7

200K

Cached-input rate

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

DeepSeek V4 Pro 0813

$0.003625 per 1M cached input tokens

MiniMax M2.7

Not published

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

DeepSeek V4 Pro 0813

Proprietary

MiniMax M2.7

Open Weight

License

DeepSeek V4 Pro 0813

Proprietary

MiniMax M2.7

Open Weight

Release date

DeepSeek V4 Pro 0813

2026-08-13

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
DeepSeek V4 Pro 0813 has the higher public score estimate, 66.39 versus 55.26, but the 90% score intervals overlap.
Workload cost
Repository review: $0.02436 vs $0.0186. Cache-heavy agent loop: $0.01812 vs $0.078.
Context tradeoff
DeepSeek V4 Pro 0813 has the larger documented window (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 evidence51 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    MiniMax M2.757%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    MiniMax M2.746.3%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek V4 Pro 081354.7%
    Source
    MiniMax M2.748.7%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • MLE-Bench Lite

    DeepSeek V4 Pro 0813—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    DeepSeek V4 Pro 0813—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Pro 0813—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Pro 0813—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    MiniMax M2.756.2%
    Source

    MiniMax M2.7 leads this result

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    MiniMax M2.776.5%
    Source

    MiniMax M2.7 leads this result

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    DeepSeek V4 Pro 081349.93%
    MiniMax M2.727.04%

    DeepSeek V4 Pro 0813 leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    MiniMax M2.739.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • DeepSWE

    DeepSeek V4 Pro 081362.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek V4 Pro 081359.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek V4 Pro 081387.5%
    Source
    MiniMax M2.779.9%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • SWE-bench (Vals)

    DeepSeek V4 Pro 081396.4%
    Source
    MiniMax M2.773.8%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • SWE-bench Verified*

    DeepSeek V4 Pro 0813—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-Rebench

    DeepSeek V4 Pro 0813—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • Multi-SWE Bench

    DeepSeek V4 Pro 0813—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    DeepSeek V4 Pro 0813—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • React Native Evals

    DeepSeek V4 Pro 0813—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    MiniMax M2.787.0%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek V4 Pro 081392.4%
    Source
    MiniMax M2.786.6%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • MMLU-Pro (Vals)

    DeepSeek V4 Pro 081387.0%
    Source
    MiniMax M2.780.4%
    Source

    DeepSeek V4 Pro 0813 leads this result

  • MMLU-Pro (Arcee)

    DeepSeek V4 Pro 0813—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    DeepSeek V4 Pro 0813—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro 0813 or MiniMax M2.7?

DeepSeek V4 Pro 0813 has the higher public score estimate, 66.39 versus 55.26, 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 V4 Pro 0813 or MiniMax M2.7?

DeepSeek V4 Pro 0813 scores higher for coding on the public lane, 51.9 to 48.8. MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V4 Pro 0813 or MiniMax M2.7?

DeepSeek V4 Pro 0813 scores higher for agentic tasks on the public lane, 56.5 to 41.3. MiniMax M2.7 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V4 Pro 0813 or MiniMax M2.7?

For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro 0813 and $0.0009 on MiniMax M2.7; repository review costs $0.02436 and $0.0186; the cache-heavy agent loop costs $0.01812 and $0.078. 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.

Which has the larger context window, DeepSeek V4 Pro 0813 or MiniMax M2.7?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 200K.

Related comparisons

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Last updated September 14, 2026

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