Command R (08-2024) vs GPT-4o-mini
Command R (08-2024) and GPT-4o-mini cost about the same per output token ($0.60 vs $0.60 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.60
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.60
Command R (08-2024) or GPT-4o-mini: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Command R (08-2024) has results on LMArena Text: coding, LMArena Text: software and IT and LMArena Text: hard prompts; GPT-4o-mini on AA Coding Index, LiveCodeBench (Vals AI) and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead.
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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-4o-mini has a AA Intelligence Index result, 6.7.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o-mini has a GPQA Diamond result, 44.2%.
Directional only
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Long documents
Listed context window
No call
Both take 128K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Command R (08-2024) $0.0004, GPT-4o-mini $0.0004 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, GPT-4o-mini not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Both cost $0.60 per million output tokens.
- Context window
- Both take 128K tokens.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | Command R (08-2024) | GPT-4o-mini | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 11.4 | Only GPT-4o-mini measured |
| LiveCodeBench (Vals AI) | Not measured | 26.4% | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 23.4% | Only GPT-4o-mini measured |
| LMArena Text: coding | 1187 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: software and IT | 1197 | Not measured | Only Command R (08-2024) measured |
| SciCode | Not measured | 1.5% | Only GPT-4o-mini measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R (08-2024) | GPT-4o-mini | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only GPT-4o-mini measured |
| Epoch Capabilities Index | Not measured | 126.5 | Only GPT-4o-mini measured |
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Command R (08-2024) | GPT-4o-mini | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 44.2% | Only GPT-4o-mini measured |
| MMMU Pro (Vals AI) | Not measured | 56.6% | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 42.6% | Only GPT-4o-mini measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.2% | Only GPT-4o-mini measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o-mini measured |
| LMArena Text: hard prompts | 1186 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: expert | 1173 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: hard prompts in English | 1194 | Not measured | Only Command R (08-2024) measured |
| Kagi LLM Benchmark | Not measured | 28.8% | Only GPT-4o-mini measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R (08-2024) | GPT-4o-mini | Leader |
|---|---|---|---|
| AIME | Not measured | 11.5% | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | Not measured | 72.6% | Only GPT-4o-mini measured |
| MGSM (Vals AI) | Not measured | 86.2% | Only GPT-4o-mini measured |
| AA Math Index | Not measured | 14.7 | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.7% | Only GPT-4o-mini measured |
| GSM8K | Not measured | 91.3% | Only GPT-4o-mini measured |
| LMArena Text: math | 1188 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: mathematical work | 1198 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards measured
| Benchmark | Command R (08-2024) | GPT-4o-mini | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 62.7% | Only GPT-4o-mini measured |
| CorpFin (Vals AI) | Not measured | 45.5% | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | Not measured | 60.5% | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | Not measured | 54.5% | Only GPT-4o-mini measured |
| MedQA (Vals AI) | Not measured | 72.4% | Only GPT-4o-mini measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 64.8% | Only GPT-4o-mini measured |
| MMLU | Not measured | 81.8% | Only GPT-4o-mini measured |
| LMArena Text: science | 1252 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: medicine and healthcare | 1238 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: legal and government | 1268 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: business and finance | 1215 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | Command R (08-2024) | GPT-4o-mini | Leader |
|---|---|---|---|
| IFBench | Not measured | 31.0% | Only GPT-4o-mini measured |
| DTBench | Not measured | 54.4% | Only GPT-4o-mini measured |
| LMCA | Not measured | 10.4% | Only GPT-4o-mini measured |
| PIQA | Not measured | 88.7% | Only GPT-4o-mini measured |
| LMArena Text | 1229 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: instruction following | 1197 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: creative writing | 1234 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: English | 1238 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: non-English | 1216 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Chinese | 1227 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: German | 1216 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Japanese | 1166 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Korean | 1162 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: Russian | 1227 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: multi-turn | 1213 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: longer queries | 1230 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: writing and language | 1241 | Not measured | Only Command R (08-2024) measured |
| LMArena Text: entertainment and media | 1223 | Not measured | Only Command R (08-2024) measured |
| Neither model has a result on the other 28 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
Same cost
Repository review
50K input + 3K output tokens
Same cost
Agent loop
200K input + 10K output tokens
Same cost
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.
| Specification | Cohere Command R (08-2024) | OpenAI GPT-4o-mini |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 30, 2024 | July 18, 2024 |
| Context window | 128K | 128K |
| Input $/Mtok | $0.15 | $0.15 |
| Output $/Mtok | $0.60 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R (08-2024) better than GPT-4o-mini for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Command R (08-2024) or GPT-4o-mini?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-4o-mini costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or GPT-4o-mini?
Neither: both take 128K tokens.
Has any benchmark tested both Command R (08-2024) and GPT-4o-mini?
No benchmark has tested both yet. Command R (08-2024) has results on LMArena Text: coding, LMArena Text: software and IT and LMArena Text: hard prompts; GPT-4o-mini on AA Coding Index, LiveCodeBench (Vals AI) and LiveCodeBench (Artificial Analysis run). Compare them on price and context instead. Data as of October 21, 2026.