Command R (08-2024) vs GPT-4
Command R (08-2024) costs 100.0x less per output token than GPT-4 ($0.60 vs $60.00 per million). Updated October 21, 2026; 25 benchmarks have a result for both.
Command R (08-2024) or GPT-4: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command R (08-2024) for output tokens 100.0x cheaper ($0.60 vs $60.00 per million) and a larger context window (128K against 8K tokens).
- Shared results
- 25
- Command R (08-2024) only
- 0
- GPT-4 only
- 20
- 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.
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-4 has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Command R (08-2024)
Command R (08-2024) takes 128K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R (08-2024)
Command R (08-2024) $0.0004, GPT-4 $0.060 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, GPT-4 not benchmarked.
- Widest category
- Knowledge, 13.1 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: medicine and healthcare: Command R (08-2024) 1238 against GPT-4 1145, 15.4 points apart on a 0 to 100 scale.
- Output price
- Command R (08-2024) $0.60 against GPT-4 $60.00 per million output tokens, 100.0x apart.
- Context window
- Command R (08-2024) takes 128K tokens, GPT-4 8K.
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.
| Category | Command R (08-2024) | Bars | GPT-4 | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Reasoning | 1 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 4 | 0 | ||
| General | 13 | 1 | ||
| All shared boards | 19 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 22.4% | Only GPT-4 measured |
| SWE-bench Lite | Not measured | 39.7% | Only GPT-4 measured |
| SWE-bench (full test set) | Not measured | 12.5% | Only GPT-4 measured |
| AA Coding Index | Not measured | 13.1 | Only GPT-4 measured |
| LMArena Text: coding | 1187 | 1209 | GPT-4 |
| LMArena Text: software and IT | 1197 | 1195 | Command R (08-2024) |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only GPT-4 measured |
| Epoch Capabilities Index | Not measured | 125.7 | Only GPT-4 measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 34.9% | Only GPT-4 measured |
| BIG-Bench Hard | Not measured | 75.1% | Only GPT-4 measured |
| LMArena Text: hard prompts | 1186 | 1200 | GPT-4 |
| LMArena Text: expert | 1173 | 1149 | Command R (08-2024) |
| LMArena Text: hard prompts in English | 1194 | 1209 | GPT-4 |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| GSM8K | Not measured | 92.0% | Only GPT-4 measured |
| LMArena Text: math | 1188 | 1230 | GPT-4 |
| LMArena Text: mathematical work | 1198 | 1228 | GPT-4 |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 56.2% | Only GPT-4 measured |
| MMLU | Not measured | 86.4% | Only GPT-4 measured |
| AI2 Reasoning Challenge | Not measured | 96.3% | Only GPT-4 measured |
| LMArena Text: science | 1252 | 1177 | Command R (08-2024) |
| LMArena Text: medicine and healthcare | 1238 | 1145 | Command R (08-2024) |
| LMArena Text: legal and government | 1268 | 1191 | Command R (08-2024) |
| LMArena Text: business and finance | 1215 | 1145 | Command R (08-2024) |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index131
| Benchmark | Command R (08-2024) | GPT-4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 33.2% | Only GPT-4 measured |
| DTBench | Not measured | 62.7% | Only GPT-4 measured |
| LMCA | Not measured | 17.1% | Only GPT-4 measured |
| HellaSwag | Not measured | 95.3% | Only GPT-4 measured |
| WinoGrande | Not measured | 87.5% | Only GPT-4 measured |
| LMArena Text | 1229 | 1206 | Command R (08-2024) |
| LMArena Text: instruction following | 1197 | 1201 | GPT-4 |
| LMArena Text: creative writing | 1234 | 1191 | Command R (08-2024) |
| LMArena Text: English | 1238 | 1220 | Command R (08-2024) |
| LMArena Text: non-English | 1216 | 1194 | Command R (08-2024) |
| LMArena Text: Chinese | 1227 | 1184 | Command R (08-2024) |
| LMArena Text: German | 1216 | 1198 | Command R (08-2024) |
| LMArena Text: Japanese | 1166 | 1137 | Command R (08-2024) |
| LMArena Text: Korean | 1162 | 1087 | Command R (08-2024) |
| LMArena Text: French | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: Spanish | Not measured | 1197 | Only GPT-4 measured |
| LMArena Text: Russian | 1227 | 1195 | Command R (08-2024) |
| LMArena Text: Polish | Not measured | 1265 | Only GPT-4 measured |
| LMArena Text: multi-turn | 1213 | 1206 | Command R (08-2024) |
| LMArena Text: longer queries | 1230 | 1190 | Command R (08-2024) |
| LMArena Text: writing and language | 1241 | 1219 | Command R (08-2024) |
| LMArena Text: entertainment and media | 1223 | 1184 | Command R (08-2024) |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command R (08-2024) | GPT-4 | 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) costs less
Repository review
50K input + 3K output tokens
Command R (08-2024) costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Cohere Command R (08-2024) | OpenAI GPT-4 |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 30, 2024 | May 28, 2023 |
| Context window | 128K | 8K |
| Input $/Mtok | $0.15 | $30.00 |
| Output $/Mtok | $0.60 | $60.00 |
| 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-4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command R (08-2024) for output tokens 100.0x cheaper ($0.60 vs $60.00 per million) and a larger context window (128K against 8K tokens).
Which is cheaper, Command R (08-2024) or GPT-4?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-4 costs $30.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or GPT-4?
Command R (08-2024), with 128K tokens against 8K.
How many benchmarks is the Command R (08-2024) vs GPT-4 comparison based on?
25 benchmarks have a result for both models as of October 21, 2026. Command R (08-2024)'s coding index rests on 0 independent benchmarks and GPT-4's on 0. Where only one model has a result, the page says so instead of naming a winner.