Enter opens the highlighted result. Esc closes.

Command R (08-2024) vs Mistral Medium 3.5

Command R (08-2024) costs 12.5x less per output token than Mistral Medium 3.5 ($0.60 vs $7.50 per million). Updated October 22, 2026; 24 benchmarks have a result for both.

Replace Command R (08-2024) Replace Mistral Medium 3.5
Command R (08-2024)Cohere
Coding
No data
Overall
No data
Output $/M
$0.60
0
Mistral Medium 3.5Mistral AI
Coding
25.5
Overall
37.3
Output $/M
$7.50
24
vs boards won of 24 shared

Command R (08-2024) or Mistral Medium 3.5: which to pick

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; Command R (08-2024) has none, so the two cannot be ranked on coding.

Pick Command R (08-2024) for output tokens 12.5x cheaper ($0.60 vs $7.50 per million).

Model ACommand R (08-2024)CohereNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMistral Medium 3.5Mistral AI25.5/1003 independent benchmarks$6.02 per solved task
Shared results
24
Command R (08-2024) only
1
Mistral Medium 3.5 only
57
Like-for-like categories
5 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Command R (08-2024) not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
Widest category
Coding, 44.6 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Text: coding: Command R (08-2024) 1187 against Mistral Medium 3.5 1460, 45.6 points apart on a 0 to 100 scale.
Output price
Command R (08-2024) $0.60 against Mistral Medium 3.5 $7.50 per million output tokens, 12.5x apart.
Context window
Command R (08-2024) takes 128K tokens, Mistral Medium 3.5 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryCommand R (08-2024)BarsMistral Medium 3.5Ties
Coding02
Reasoning03
Math02
Knowledge04
General013
All shared boards024
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
LMArena WebDevNot measured1264Only Mistral Medium 3.5 measured
AA Coding IndexNot measured46.9Only Mistral Medium 3.5 measured
SWE-bench Verified (Vals AI)Not measured66.4%Only Mistral Medium 3.5 measured
Vibe Code Bench (Vals AI)Not measured2.9%Only Mistral Medium 3.5 measured
Code migration (Vals AI)Not measured5.1%Only Mistral Medium 3.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured39.0%Only Mistral Medium 3.5 measured
SciCodeNot measured40.2%Only Mistral Medium 3.5 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured33.3%Only Mistral Medium 3.5 measured
ALE-BenchNot measured764.0Only Mistral Medium 3.5 measured
LMArena Text: coding11871460Mistral Medium 3.5
LMArena Text: software and IT11971458Mistral Medium 3.5
LMArena WebDev: ReactNot measured1254Only Mistral Medium 3.5 measured
LMArena WebDev: HTMLNot measured1308Only Mistral Medium 3.5 measured
Neither model has a result on the other 26 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
AA Intelligence IndexNot measured14.2Only Mistral Medium 3.5 measured
Epoch Capabilities IndexNot measured141.4Only Mistral Medium 3.5 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
AA Agentic IndexNot measured7.0Only Mistral Medium 3.5 measured
Finance Agent (Vals AI)Not measured32.1%Only Mistral Medium 3.5 measured
Tax Agent Bench (Vals AI)Not measured4.5%Only Mistral Medium 3.5 measured
Legal Research Bench (Vals AI)Not measured9.1%Only Mistral Medium 3.5 measured
Excel Modeling Benchmark (Vals AI)Not measured11.2%Only Mistral Medium 3.5 measured
τ²-Bench TelecomNot measured94.2%Only Mistral Medium 3.5 measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
GPQA DiamondNot measured34.9%Only Mistral Medium 3.5 measured
GPQA Diamond (Artificial Analysis run)Not measured74.8%Only Mistral Medium 3.5 measured
Humanity's Last Exam (Artificial Analysis run)Not measured13.8%Only Mistral Medium 3.5 measured
LMArena Text: hard prompts11861436Mistral Medium 3.5
LMArena Text: expert11731432Mistral Medium 3.5
LMArena Text: hard prompts in English11941445Mistral Medium 3.5
LMArena Vision: diagramsNot measured1254Only Mistral Medium 3.5 measured
LMArena Vision: homeworkNot measured1230Only Mistral Medium 3.5 measured
Kagi LLM BenchmarkNot measured41.4%Only Mistral Medium 3.5 measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
ProofBenchNot measured9.0%Only Mistral Medium 3.5 measured
LMArena Text: math11881431Mistral Medium 3.5
LMArena Text: mathematical work11981440Mistral Medium 3.5
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
MMLU ProNot measured75.3%Only Mistral Medium 3.5 measured
CaseLaw (Vals AI)Not measured44.2%Only Mistral Medium 3.5 measured
CorpFin (Vals AI)Not measured58.8%Only Mistral Medium 3.5 measured
TaxEval (Vals AI)Not measured68.0%Only Mistral Medium 3.5 measured
MortgageTax (Vals AI)Not measured28.9%Only Mistral Medium 3.5 measured
MedCode (Vals AI)Not measured33.8%Only Mistral Medium 3.5 measured
LMArena Text: science12521428Mistral Medium 3.5
LMArena Text: medicine and healthcare12381409Mistral Medium 3.5
LMArena Text: legal and government12681437Mistral Medium 3.5
LMArena Text: business and finance12151426Mistral Medium 3.5
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
Design Arena: websitesNot measured1142Only Mistral Medium 3.5 measured
Design Arena: UI componentsNot measured1076Only Mistral Medium 3.5 measured
Design Arena: game devNot measured1079Only Mistral Medium 3.5 measured
Design Arena: data vizNot measured1120Only Mistral Medium 3.5 measured
Design Arena: 3DNot measured1060Only Mistral Medium 3.5 measured
Design Arena: all code categoriesNot measured1121Only Mistral Medium 3.5 measured
Design Arena: SVGNot measured949Only Mistral Medium 3.5 measured
Design Arena: ASCII artNot measured1062Only Mistral Medium 3.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
General benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
SAGE (Vals AI)Not measured37.6%Only Mistral Medium 3.5 measured
MedScribe (Vals AI)Not measured67.7%Only Mistral Medium 3.5 measured
Vals Multimodal IndexNot measured34.8%Only Mistral Medium 3.5 measured
IFBenchNot measured68.8%Only Mistral Medium 3.5 measured
Long context reasoning (AA-LCR)Not measured69.3%Only Mistral Medium 3.5 measured
DTBenchNot measured75.5%Only Mistral Medium 3.5 measured
LMCANot measured26.1%Only Mistral Medium 3.5 measured
Surface Evolver BenchNot measured26.9%Only Mistral Medium 3.5 measured
LMArena Text12291421Mistral Medium 3.5
LMArena Text: instruction following11971415Mistral Medium 3.5
LMArena Text: creative writing12341374Mistral Medium 3.5
LMArena VisionNot measured1222Only Mistral Medium 3.5 measured
LMArena Text: English12381433Mistral Medium 3.5
LMArena Text: non-English12161404Mistral Medium 3.5
LMArena Text: Chinese12271444Mistral Medium 3.5
LMArena Text: German12161453Mistral Medium 3.5
LMArena Text: Japanese1166Not measuredOnly Command R (08-2024) measured
LMArena Text: Korean11621384Mistral Medium 3.5
LMArena Text: FrenchNot measured1448Only Mistral Medium 3.5 measured
LMArena Text: SpanishNot measured1411Only Mistral Medium 3.5 measured
LMArena Text: Russian12271395Mistral Medium 3.5
LMArena Text: PolishNot measured1430Only Mistral Medium 3.5 measured
LMArena Text: multi-turn12131423Mistral Medium 3.5
LMArena Text: longer queries12301415Mistral Medium 3.5
LMArena Text: writing and language12411384Mistral Medium 3.5
LMArena Text: entertainment and media12231382Mistral Medium 3.5
LMArena Vision: EnglishNot measured1240Only Mistral Medium 3.5 measured
LMArena Vision: ChineseNot measured1244Only Mistral Medium 3.5 measured
LMArena Vision: reading textNot measured1233Only Mistral Medium 3.5 measured
LMArena Vision: humorNot measured1216Only Mistral Medium 3.5 measured
LMArena Vision: creative writingNot measured1198Only Mistral Medium 3.5 measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Command R (08-2024) and Mistral Medium 3.5
BenchmarkCommand R (08-2024)Mistral Medium 3.5Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Command R (08-2024)$0.0004Fits in one request
Mistral Medium 3.5$0.0053Fits in one request

Command R (08-2024) costs less

Repository review

50K input + 3K output tokens

Command R (08-2024)$0.0093Fits in one request
Mistral Medium 3.5$0.098Fits in one request

Command R (08-2024) costs less

Agent loop

200K input + 10K output tokens

Command R (08-2024)$0.036Exceeds the context window
Mistral Medium 3.5$0.375Fits in one request

Command R (08-2024) costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Command R (08-2024) and Mistral Medium 3.5
SpecificationCohere Command R (08-2024)Mistral Medium 3.5
MakerCohereMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedAugust 30, 2024April 30, 2026
Context window128K262K
Input $/Mtok$0.15$1.50
Output $/Mtok$0.60$7.50
Coding indexNot benchmarked25.5
Independent measurements behind it03
Cost per solved taskNot computable$6.02
SourceOpenRouter listingOpenRouter listing

Questions

Is Command R (08-2024) better than Mistral Medium 3.5 for coding?

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; Command R (08-2024) has none, so the two cannot be ranked on coding. Pick Command R (08-2024) for output tokens 12.5x cheaper ($0.60 vs $7.50 per million).

Which is cheaper, Command R (08-2024) or Mistral Medium 3.5?

Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, Command R (08-2024) or Mistral Medium 3.5?

Mistral Medium 3.5, with 262K tokens against 128K.

How many benchmarks is the Command R (08-2024) vs Mistral Medium 3.5 comparison based on?

24 benchmarks have a result for both models as of October 22, 2026. Command R (08-2024)'s coding index rests on 0 independent benchmarks and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

Compare next