Noometry

Model comparison

DeepSeek-V3.1-Terminus vs Mistral Nemo

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 25.9 on the Noometry Index. Mistral Nemo costs 3.2× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

Last verified . 1 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

42.6

Rank #96 Confirmed

Mistral Nemo Mistral AI

25.9

Rank #332 Confirmed

Summary

  • They share 1 benchmark with published results for both. DeepSeek-V3.1-Terminus scores higher in 3 categories and Mistral Nemo in 0 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.3 to 28.6.
  • The biggest single-benchmark swing is DTBench: 81.3% for DeepSeek-V3.1-Terminus and 48.6% for Mistral Nemo.
  • Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $0.30 / $1 for DeepSeek-V3.1-Terminus.
  • DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3.1-TerminusMistral Nemo
ProviderDeepSeekMistral AI
Noometry Index42.625.9
Released2025-09-222024-07-01
WeightsOpenOpen
Context window164K128K
Max output66K128K
Input $ / M tokens$0.30$0.15
Output $ / M tokens$1$0.15
Results tracked1610

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Category by category

Coding Not comparable

DeepSeek-V3.1-Terminus: 41.5 (#114), Mistral Nemo: —

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
SciCode40.6%—
LMArena Coding1426—
ALE-Bench745.17—

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, Mistral Nemo: 23.3 (#118)

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
Berkeley Function Calling Leaderboard—27.6%
BALROG—17.6%

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 25.7 (#126), Mistral Nemo: 19.9 (#230)

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
DTBench81.3%48.6%
Kagi LLM Benchmark57.4%—
CritPt1.7%—
LMArena Hard Prompts1427—
LMCA28.6%—
Epoch Capabilities Index—118.63
PIQA—83.5%

Math DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 35.7 (#138), Mistral Nemo: 23.5 (#263)

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
LMArena Math1402—
MATH Level 5—10.8%
GSM8K—84.2%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Mistral Nemo: 12.3 (#293)

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
GPQA Diamond—29.9%
BoolQ—82.5%

Multilingual Not comparable

DeepSeek-V3.1-Terminus: 52.3 (#87), Mistral Nemo: —

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
LMArena Non-English1407—
LMArena Russian1434—

Instruction Following Not comparable

DeepSeek-V3.1-Terminus: 74.4 (#96), Mistral Nemo: —

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
LMArena Instruction Following1406—

Long Context Not comparable

DeepSeek-V3.1-Terminus: 43.5 (#88), Mistral Nemo: —

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
LMArena Longer Query1426—

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.3 (#87), Mistral Nemo: 28.6 (#291)

BenchmarkDeepSeek-V3.1-TerminusMistral Nemo
LMArena Text1420—
LMArena Creative Writing1404—
EQ-Bench Creative Writing—881
LMArena Multi-Turn1411—

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than Mistral Nemo?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 25.9 on the Noometry Index. Mistral Nemo costs 3.2× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

Which is cheaper, DeepSeek-V3.1-Terminus or Mistral Nemo?

Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.30 and $1.

Which has the bigger context window?

DeepSeek-V3.1-Terminus does, with 164K tokens against 128K.

How many benchmarks do DeepSeek-V3.1-Terminus and Mistral Nemo share?

1 benchmark has published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Mistral Nemo has 10.

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