DeepSeek-R1 vs MiniMax M2.7

Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.

MiniMax M2.7 is clearly ahead on the aggregate, 57 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

MiniMax M2.7's sharpest advantage is in coding, where it averages 56.2 against 26.1. The single biggest benchmark swing on the page is SWE-bench Pro, 25% to 56.2%.

DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. DeepSeek-R1 is the reasoning model in the pair, while MiniMax M2.7 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. MiniMax M2.7 gives you the larger context window at 200K, compared with 128K for DeepSeek-R1.

Quick Verdict

Pick MiniMax M2.7 if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if you want the stronger reasoning-first profile.

Agentic

MiniMax M2.7

DeepSeek-R1

44.5

MiniMax M2.7

57

42%
Terminal-Bench 2.0
57%
49%
BrowseComp
Coming soon
44%
OSWorld-Verified
Coming soon
Coming soon
Toolathlon
46.3%
Coming soon
MLE-Bench Lite
66.6%
Coming soon
MM-ClawBench
62.7%

Coding

MiniMax M2.7

DeepSeek-R1

26.1

MiniMax M2.7

56.2

92%
HumanEval
Coming soon
49.2%
SWE-bench Verified
Coming soon
19%
LiveCodeBench
Coming soon
25%
SWE-bench Pro
56.2%
Coming soon
SWE Multilingual
76.5%
Coming soon
Multi-SWE Bench
52.7%
Coming soon
VIBE-Pro
55.6%
Coming soon
NL2Repo
39.8%

Multimodal & Grounded

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

43%
MMMU-Pro
Coming soon
53%
OfficeQA Pro
Coming soon
Coming soon
GDPval-AA
1495

Reasoning

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

40%
MuSR
Coming soon
66%
BBH
Coming soon
58%
LongBench v2
Coming soon
57%
MRCRv2
Coming soon
1.3%
ARC-AGI-2
Coming soon

Knowledge

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

90.8%
MMLU
Coming soon
71.5%
GPQA
Coming soon
41%
SuperGPQA
Coming soon
84%
MMLU-Pro
Coming soon
14%
HLE
Coming soon
44%
FrontierScience
Coming soon
30.1%
SimpleQA
Coming soon
Coming soon
Artificial Analysis
50

Instruction Following

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

83.3%
IFEval
Coming soon

Multilingual

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

61%
MGSM
Coming soon
60%
MMLU-ProX
Coming soon

Mathematics

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

44%
AIME 2023
Coming soon
79.8%
AIME 2024
Coming soon
45%
AIME 2025
Coming soon
40%
HMMT Feb 2023
Coming soon
42%
HMMT Feb 2024
Coming soon
41%
HMMT Feb 2025
Coming soon
43%
BRUMO 2025
Coming soon
97.3%
MATH-500
Coming soon

Frequently Asked Questions

Which is better, DeepSeek-R1 or MiniMax M2.7?

MiniMax M2.7 is ahead overall, 57 to 44. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 25% and 56.2%.

Which is better for coding, DeepSeek-R1 or MiniMax M2.7?

MiniMax M2.7 has the edge for coding in this comparison, averaging 56.2 versus 26.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, DeepSeek-R1 or MiniMax M2.7?

MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 44.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

Last updated: March 18, 2026

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