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DeepSeek-R1 vs MiniMax M3

Updated October 14, 2026. Rank says MiniMax M3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

MiniMax M3 has the higher public score estimate, 55 versus 41.12, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
DeepSeek logo
DeepSeek-R1

DeepSeek

41.12/100

Supported · Public rank #127

90% interval 26.9–55.3

Model B
MiniMax logo
MiniMax M3

MiniMax

55/100

Supported · Public rank #68

90% interval 45.6–64.4

Shared results
1
DeepSeek-R1 only
7
MiniMax M3 only
26
Like-for-like categories
1 / 8
Supported: DeepSeek-R1 and 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.

  • 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

    MiniMax M3

    MiniMax M3 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 M3

    MiniMax M3 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

    DeepSeek-R1 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

    DeepSeek-R1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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-R1 does not fit this workload in one request.

    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.

—DeepSeek-R136.5MiniMax M3

Not comparable · BenchAlign v5.8

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.

Open the coding leaderboardShortlist models for this job
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.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Knowledge

Like-for-like
DeepSeek-R1
41.4
Supported · #109/181
MiniMax M3
49.6
Supported · #80/181
Basis
BenchAlign v5.8 lane · 4 vs 2 public rows
Reading
MiniMax M3 leads · intervals overlap

Instruction following

Directional only
DeepSeek-R1
45.9
#99/144
MiniMax M3
93.8
#5/144
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
DeepSeek-R1
Not ranked
MiniMax M3
39.6
Supported · #66/125
Basis
BenchAlign v5.8 lane · 0 vs 9 public rows
Reading
Not comparable

Coding

Not comparable
DeepSeek-R1
Not ranked
MiniMax M3
36.5
Supported · #73/149
Basis
BenchAlign v5.8 lane · 1 vs 10 public rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek-R1
64.1
Unranked · 2 rankable rows
MiniMax M3
82.9
#5/29
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek-R1
Not ranked
MiniMax M3
61.5
#43/54
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek-R1
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
DeepSeek-R1
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 v5.8) 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

DeepSeek-R1
$0.00165
Fits in one request
MiniMax M3
$0.0009
Fits in one request

MiniMax M3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek-R1
$0.03407
Fits in one request
MiniMax M3
$0.0186
Fits in one request

MiniMax M3 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-R1
$0.0609
Does not fit in one request
MiniMax M3
$0.03
Fits in one request

DeepSeek-R1 does not fit this workload in one request.

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.

DeepSeek-R1

128K

MiniMax M3

1M

API model ID

DeepSeek-R1

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-R1

$0.14 per 1M cached input tokens

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

DeepSeek-R1

Not sourced

MiniMax M3

Not sourced

Documented outputs

DeepSeek-R1

Not sourced

MiniMax M3

Not sourced

Provider availability

DeepSeek-R1

Not sourced

MiniMax M3

Not sourced

Reasoning profile

DeepSeek-R1

Reasoning

MiniMax M3

Non-Reasoning

Weight access

DeepSeek-R1

Open Weight

MiniMax M3

Open Weight

License

DeepSeek-R1

Open Weight

MiniMax M3

Open Weight

Release date

DeepSeek-R1

2025-01-20

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, 55 versus 41.12, but the 90% score intervals overlap.
Workload cost
Repository review: $0.03407 vs $0.0186. Cache-heavy agent loop: $0.0609 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.

Questions

Which is better, DeepSeek-R1 or MiniMax M3?

MiniMax M3 has the higher public score estimate, 55 versus 41.12, 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-R1 or MiniMax M3?

DeepSeek-R1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, DeepSeek-R1 or MiniMax M3?

DeepSeek-R1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek-R1 or MiniMax M3?

For the stated presets, chat costs $0.00164 on DeepSeek-R1 and $0.0009 on MiniMax M3; repository review costs $0.03407 and $0.0186; the cache-heavy agent loop costs $0.0609 and $0.03. DeepSeek-R1 does not fit this workload in one request.

Which has the larger context window, DeepSeek-R1 or MiniMax M3?

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

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek-R1
API / mo$2,055
Self-host / mo$18,221
Break-even583M/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 evidence34 rows

Agentic

  • Terminal-Bench 2.1

    DeepSeek-R1—
    MiniMax M366.0%
    Source

    Not directly comparable

  • BrowseComp

    DeepSeek-R1—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    DeepSeek-R1—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    DeepSeek-R1—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    DeepSeek-R1—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    DeepSeek-R1—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    DeepSeek-R1—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    DeepSeek-R1—
    MiniMax M34.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    DeepSeek-R1—
    MiniMax M353.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    DeepSeek-R149.2%
    Source
    MiniMax M380.5%
    Source

    MiniMax M3 leads this result

  • SWE-bench Pro

    DeepSeek-R1—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek-R1—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek-R1—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    DeepSeek-R1—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    DeepSeek-R1—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    DeepSeek-R1—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    DeepSeek-R1—
    MiniMax M348.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    DeepSeek-R1—
    MiniMax M382.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    DeepSeek-R1—
    MiniMax M375.0%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    DeepSeek-R1—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    DeepSeek-R1—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek-R1—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    DeepSeek-R1—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    DeepSeek-R1—
    MiniMax M385.4%
    Source

    Not directly comparable

Knowledge

  • MMLU

    DeepSeek-R190.8%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA

    DeepSeek-R171.5%
    Source
    MiniMax M3—

    Not directly comparable

  • MMLU-Pro

    DeepSeek-R184%
    Source
    MiniMax M3—

    Not directly comparable

  • SimpleQA

    DeepSeek-R130.1%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA Diamond (Vals)

    DeepSeek-R1—
    MiniMax M392.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    DeepSeek-R1—
    MiniMax M384.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    DeepSeek-R183.3%
    Source
    MiniMax M3—

    Not directly comparable

Math

  • AIME 2024

    DeepSeek-R179.8%
    Source
    MiniMax M3—

    Not directly comparable

  • MATH-500

    DeepSeek-R197.3%
    Source
    MiniMax M3—

    Not directly comparable

  • USAMO 2026

    DeepSeek-R1—
    MiniMax M385.7%
    Source

    Not directly comparable

34 public results · 1 shared

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Last updated October 14, 2026