Enter opens the highlighted result. Esc closes.

Command R (08-2024) vs Qwen3.8 Max (0902)

Command R (08-2024) costs 10.0x less per output token than Qwen3.8 Max (0902) ($0.60 vs $6.00 per million). Updated October 22, 2026; 25 benchmarks have a result for both.

Command R (08-2024)Cohere
Coding
No data
Overall
No data
Output $/M
$0.60
0
Qwen3.8 Max (0902)Qwen
Coding
62.4
Overall
68.9
Output $/M
$6.00
25
vs boards won of 25 shared

Command R (08-2024) or Qwen3.8 Max (0902): which to pick

Only Qwen3.8 Max (0902) has a coding score, 62.4 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 10.0x cheaper ($0.60 vs $6.00 per million).

Shared results
25
Command R (08-2024) only
0
Qwen3.8 Max (0902) only
88
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, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
Widest category
Reasoning, 52.9 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: expert: Command R (08-2024) 1173 against Qwen3.8 Max (0902) 1507, 55.5 points apart on a 0 to 100 scale.
Output price
Command R (08-2024) $0.60 against Qwen3.8 Max (0902) $6.00 per million output tokens, 10.0x apart.
Context window
Command R (08-2024) takes 128K tokens, Qwen3.8 Max (0902) 1M.

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)BarsQwen3.8 Max (0902)Ties
Coding02
Reasoning03
Math02
Knowledge04
General014
All shared boards025
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
LiveBench codingNot measured60.3%Only Qwen3.8 Max (0902) measured
LMArena WebDevNot measured1674Only Qwen3.8 Max (0902) measured
AA Coding IndexNot measured76.2Only Qwen3.8 Max (0902) measured
LiveCodeBench (Vals AI)Not measured87.8%Only Qwen3.8 Max (0902) measured
SWE-bench Verified (Vals AI)Not measured85.6%Only Qwen3.8 Max (0902) measured
Terminal-Bench 4 (Vals AI)Not measured34.3%Only Qwen3.8 Max (0902) measured
Vibe Code Bench (Vals AI)Not measured64.7%Only Qwen3.8 Max (0902) measured
Code migration (Vals AI)Not measured24.0%Only Qwen3.8 Max (0902) measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 Max (0902) measured
IOI (Vals AI)Not measured68.9%Only Qwen3.8 Max (0902) measured
Terminal-Bench 2.1 (Vals AI)Not measured67.4%Only Qwen3.8 Max (0902) measured
SWE-bench Multimodal (Vals AI)Not measured42.5%Only Qwen3.8 Max (0902) measured
Vibe Code Bench 1-100 (Vals AI)Not measured12.8%Only Qwen3.8 Max (0902) measured
SkillsBench (Vals AI)Not measured42.0%Only Qwen3.8 Max (0902) measured
CyberBench (Vals AI)Not measured28.6%Only Qwen3.8 Max (0902) measured
SciCodeNot measured53.2%Only Qwen3.8 Max (0902) measured
FrontierSWENot measured17.8%Only Qwen3.8 Max (0902) measured
LMArena Text: coding11871502Qwen3.8 Max (0902)
LMArena Text: software and IT11971498Qwen3.8 Max (0902)
LMArena WebDev: ReactNot measured1692Only Qwen3.8 Max (0902) measured
LMArena WebDev: HTMLNot measured1649Only Qwen3.8 Max (0902) measured
LMArena WebDev: from an imageNot measured1637Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 17 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
AA Intelligence IndexNot measured45.4Only Qwen3.8 Max (0902) measured
Epoch Capabilities IndexNot measured157.4Only Qwen3.8 Max (0902) measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
Agentic work benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
AA Agentic IndexNot measured56.0Only Qwen3.8 Max (0902) measured
Design Arena: agents building web appsNot measured1300Only Qwen3.8 Max (0902) measured
Design Arena: agents building full-stack appsNot measured1305Only Qwen3.8 Max (0902) measured
Design Arena: agents building mobile appsNot measured1224Only Qwen3.8 Max (0902) measured
Design Arena: agents building Android appsNot measured1196Only Qwen3.8 Max (0902) measured
Design Arena: agents building Godot gamesNot measured1234Only Qwen3.8 Max (0902) measured
Design Arena: PowerPoint slidesNot measured1233Only Qwen3.8 Max (0902) measured
Design Arena: spreadsheetsNot measured967Only Qwen3.8 Max (0902) measured
LiveBench researchNot measured69.9%Only Qwen3.8 Max (0902) measured
LiveBench browserNot measured32.0%Only Qwen3.8 Max (0902) measured
LiveBench long agenticNot measured42.3%Only Qwen3.8 Max (0902) measured
Finance Agent (Vals AI)Not measured50.6%Only Qwen3.8 Max (0902) measured
Tax Agent Bench (Vals AI)Not measured32.1%Only Qwen3.8 Max (0902) measured
Legal Research Bench (Vals AI)Not measured47.6%Only Qwen3.8 Max (0902) measured
Excel Modeling Benchmark (Vals AI)Not measured60.1%Only Qwen3.8 Max (0902) measured
Terminal-Bench-Science (Vals AI)Not measured12.9%Only Qwen3.8 Max (0902) measured
APEX-AgentsNot measured63.3%Only Qwen3.8 Max (0902) measured
LMArena SearchNot measured1261Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
GPQA DiamondNot measured93.7%Only Qwen3.8 Max (0902) measured
LiveBench reasoningNot measured88.2%Only Qwen3.8 Max (0902) measured
MysteryMechanism (Vals AI)Not measured23.9%Only Qwen3.8 Max (0902) measured
MMMU Pro (Vals AI)Not measured88.0%Only Qwen3.8 Max (0902) measured
GPQA Diamond (Artificial Analysis run)Not measured92.8%Only Qwen3.8 Max (0902) measured
Humanity's Last Exam (Artificial Analysis run)Not measured43.1%Only Qwen3.8 Max (0902) measured
GPQA Diamond (Epoch AI run)Not measured92.7%Only Qwen3.8 Max (0902) measured
Chess puzzlesNot measured46.0%Only Qwen3.8 Max (0902) measured
Mystery game puzzlesNot measured38.0%Only Qwen3.8 Max (0902) measured
LMArena Text: hard prompts11861496Qwen3.8 Max (0902)
LMArena Text: expert11731507Qwen3.8 Max (0902)
LMArena Text: hard prompts in English11941503Qwen3.8 Max (0902)
LMArena Vision: diagramsNot measured1323Only Qwen3.8 Max (0902) measured
LMArena Vision: homeworkNot measured1336Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
OTIS Mock AIMENot measured100.0%Only Qwen3.8 Max (0902) measured
FrontierMath, tiers 1 to 3Not measured74.7%Only Qwen3.8 Max (0902) measured
FrontierMath, tier 4Not measured46.3%Only Qwen3.8 Max (0902) measured
ProofBenchNot measured58.0%Only Qwen3.8 Max (0902) measured
LMArena Text: math11881501Qwen3.8 Max (0902)
LMArena Text: mathematical work11981491Qwen3.8 Max (0902)
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
MMLU ProNot measured88.6%Only Qwen3.8 Max (0902) measured
LegalBench (Vals AI)Not measured83.6%Only Qwen3.8 Max (0902) measured
CorpFin (Vals AI)Not measured65.8%Only Qwen3.8 Max (0902) measured
TaxEval (Vals AI)Not measured75.5%Only Qwen3.8 Max (0902) measured
MortgageTax (Vals AI)Not measured64.0%Only Qwen3.8 Max (0902) measured
MedCode (Vals AI)Not measured40.7%Only Qwen3.8 Max (0902) measured
SimpleQA VerifiedNot measured47.3%Only Qwen3.8 Max (0902) measured
LMArena Text: science12521512Qwen3.8 Max (0902)
LMArena Text: medicine and healthcare12381503Qwen3.8 Max (0902)
LMArena Text: legal and government12681492Qwen3.8 Max (0902)
LMArena Text: business and finance12151480Qwen3.8 Max (0902)
Neither model has a result on the other 7 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 Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
Design Arena: websitesNot measured1275Only Qwen3.8 Max (0902) measured
Design Arena: UI componentsNot measured1338Only Qwen3.8 Max (0902) measured
Design Arena: game devNot measured1312Only Qwen3.8 Max (0902) measured
Design Arena: data vizNot measured1281Only Qwen3.8 Max (0902) measured
Design Arena: 3DNot measured1336Only Qwen3.8 Max (0902) measured
Design Arena: all code categoriesNot measured1293Only Qwen3.8 Max (0902) measured
Design Arena: ASCII artNot measured1200Only Qwen3.8 Max (0902) measured
LiveBench designNot measured78.6%Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
General benchmarks for Command R (08-2024) and Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
LiveBench data analysisNot measured78.4%Only Qwen3.8 Max (0902) measured
Public Benefits Bench (Vals AI)Not measured67.1%Only Qwen3.8 Max (0902) measured
SAGE (Vals AI)Not measured51.2%Only Qwen3.8 Max (0902) measured
MedScribe (Vals AI)Not measured85.0%Only Qwen3.8 Max (0902) measured
Vals Multimodal IndexNot measured65.4%Only Qwen3.8 Max (0902) measured
Long context reasoning (AA-LCR)Not measured80.3%Only Qwen3.8 Max (0902) measured
DTBenchNot measured92.0%Only Qwen3.8 Max (0902) measured
LMCANot measured46.2%Only Qwen3.8 Max (0902) measured
Furniture assemblyNot measured20.0%Only Qwen3.8 Max (0902) measured
LMArena Text12291483Qwen3.8 Max (0902)
LMArena Text: instruction following11971481Qwen3.8 Max (0902)
LMArena Text: creative writing12341477Qwen3.8 Max (0902)
LMArena VisionNot measured1313Only Qwen3.8 Max (0902) measured
LMArena Text: English12381491Qwen3.8 Max (0902)
LMArena Text: non-English12161473Qwen3.8 Max (0902)
LMArena Text: Chinese12271539Qwen3.8 Max (0902)
LMArena Text: German12161478Qwen3.8 Max (0902)
LMArena Text: Japanese11661473Qwen3.8 Max (0902)
LMArena Text: Korean11621460Qwen3.8 Max (0902)
LMArena Text: FrenchNot measured1498Only Qwen3.8 Max (0902) measured
LMArena Text: SpanishNot measured1489Only Qwen3.8 Max (0902) measured
LMArena Text: Russian12271482Qwen3.8 Max (0902)
LMArena Text: PolishNot measured1471Only Qwen3.8 Max (0902) measured
LMArena Text: multi-turn12131489Qwen3.8 Max (0902)
LMArena Text: longer queries12301488Qwen3.8 Max (0902)
LMArena Text: writing and language12411477Qwen3.8 Max (0902)
LMArena Text: entertainment and media12231464Qwen3.8 Max (0902)
LMArena Vision: EnglishNot measured1311Only Qwen3.8 Max (0902) measured
LMArena Vision: ChineseNot measured1358Only Qwen3.8 Max (0902) measured
LMArena Vision: reading textNot measured1322Only Qwen3.8 Max (0902) measured
LMArena Vision: humorNot measured1304Only Qwen3.8 Max (0902) measured
LMArena Vision: creative writingNot measured1312Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 14 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 Qwen3.8 Max (0902)
BenchmarkCommand R (08-2024)Qwen3.8 Max (0902)Leader
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
Qwen3.8 Max (0902)$0.0050Fits 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
Qwen3.8 Max (0902)$0.118Fits 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
Qwen3.8 Max (0902)$0.460Fits 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 Qwen3.8 Max (0902)
SpecificationCohere Command R (08-2024)Qwen3.8 Max (0902)
MakerCohereQwen
WeightsNo public weights listedNo public weights listed
ReleasedAugust 30, 2024September 3, 2026
Context window128K1M
Input $/Mtok$0.15$2.00
Output $/Mtok$0.60$6.00
Coding indexNot benchmarked62.4
Independent measurements behind it05
Cost per solved taskNot computable$1.32
SourceOpenRouter listingOpenRouter listing

Questions

Is Command R (08-2024) better than Qwen3.8 Max (0902) for coding?

Only Qwen3.8 Max (0902) has a coding score, 62.4 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 10.0x cheaper ($0.60 vs $6.00 per million).

Which is cheaper, Command R (08-2024) or Qwen3.8 Max (0902)?

Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Cost per solved task is known for Qwen3.8 Max (0902) only.

Which has the bigger context window, Command R (08-2024) or Qwen3.8 Max (0902)?

Qwen3.8 Max (0902), with 1M tokens against 128K.

How many benchmarks is the Command R (08-2024) vs Qwen3.8 Max (0902) comparison based on?

25 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 Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.

Compare next