Noometry

Model comparison

DeepSeek-V3.1-Terminus vs Magistral Medium

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 34.8 on the Noometry Index.

Last verified . 13 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

42.6

Rank #96 Confirmed

Magistral Medium Mistral AI

34.8

Rank #224 Confirmed

Summary

  • They share 13 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Magistral Medium in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where DeepSeek-V3.1-Terminus leads 25.7 to 8.7.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 57.4% for DeepSeek-V3.1-Terminus and 16.2% for Magistral Medium.
  • DeepSeek-V3.1-Terminus is cheaper at $0.30 / $1 per million input/output tokens, against $2 / $5 for Magistral Medium.
  • DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 128K.

Side by side

DeepSeek-V3.1-TerminusMagistral Medium
ProviderDeepSeekMistral AI
Noometry Index42.634.8
Released2025-09-222025-03-17
WeightsOpenOpen
Context window164K128K
Max output66K16K
Input $ / M tokens$0.30$2
Output $ / M tokens$1$5
Results tracked1622

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Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 41.5 (#114), Magistral Medium: 38.6 (#161)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
SciCode40.6%39.2%
LMArena Coding14261319
ALE-Bench745.17—

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 25.7 (#126), Magistral Medium: 8.7 (#342)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
Kagi LLM Benchmark57.4%16.2%
CritPt1.7%0.3%
LMArena Hard Prompts14271267
ARC-AGI-2—0%
ARC-AGI-1—6.1%
DTBench81.3%—
LMCA28.6%—

Math DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 35.7 (#138), Magistral Medium: 32.4 (#187)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
LMArena Math14021249

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Magistral Medium: 32.9 (#199)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
LMArena Expert—1222

Multilingual DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 52.3 (#87), Magistral Medium: 39.8 (#219)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
LMArena Non-English14071232
LMArena Russian14341224
LMArena Chinese—1225
LMArena French—1268
LMArena German—1248
LMArena Japanese—1176
LMArena Korean—1125
LMArena Spanish—1269

Instruction Following DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 74.4 (#96), Magistral Medium: 66.2 (#207)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
LMArena Instruction Following14061254

Long Context DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 43.5 (#88), Magistral Medium: 39.3 (#178)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
LMArena Longer Query14261295

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.3 (#87), Magistral Medium: 46.5 (#214)

BenchmarkDeepSeek-V3.1-TerminusMagistral Medium
LMArena Text14201255
LMArena Creative Writing14041245
LMArena Multi-Turn14111275

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than Magistral Medium?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 34.8 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1-Terminus or Magistral Medium?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.30 per million input tokens and $1 per million output tokens; Magistral Medium lists at $2 and $5.

Is DeepSeek-V3.1-Terminus or Magistral Medium better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 38.6 in the Noometry coding category.

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 Magistral Medium share?

13 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Magistral Medium has 22.

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