Command R (08-2024) vs GPT-3.5 Turbo
Command R (08-2024) costs 2.5x less per output token than GPT-3.5 Turbo ($0.60 vs $1.50 per million). Updated October 22, 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
- $1.50
Command R (08-2024) or GPT-3.5 Turbo: 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 2.5x cheaper ($0.60 vs $1.50 per million) and a larger context window (128K against 16K tokens).
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-3.5 Turbo on AA Coding Index, SciCode and AA Intelligence Index. 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-3.5 Turbo has a AA Intelligence Index result, 5.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
Command R (08-2024)
Command R (08-2024) takes 128K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R (08-2024)
Command R (08-2024) $0.0004, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- Command R (08-2024) not benchmarked, GPT-3.5 Turbo 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
- Command R (08-2024) $0.60 against GPT-3.5 Turbo $1.50 per million output tokens, 2.5x apart.
- Context window
- Command R (08-2024) takes 128K tokens, GPT-3.5 Turbo 16K.
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 Index4 boards measured
| Benchmark | Command R (08-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 10.7 | Only GPT-3.5 Turbo 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 | 0.0% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Command R (08-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 5.5 | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Command R (08-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 30.6% | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 29.7% | Only GPT-3.5 Turbo measured |
| BIG-Bench Hard | Not measured | 61.6% | Only GPT-3.5 Turbo 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 |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Command R (08-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo 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 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R (08-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 64.4% | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | Not measured | 58.5% | Only GPT-3.5 Turbo measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 46.2% | Only GPT-3.5 Turbo measured |
| MMLU | Not measured | 68.5% | Only GPT-3.5 Turbo 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 10 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index16 boards measured
| Benchmark | Command R (08-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| DTBench | Not measured | 48.5% | Only GPT-3.5 Turbo measured |
| LMCA | Not measured | 9.7% | Only GPT-3.5 Turbo 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 30 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-3.5 Turbo |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 30, 2024 | May 28, 2023 |
| Context window | 128K | 16K |
| Input $/Mtok | $0.15 | $0.50 |
| Output $/Mtok | $0.60 | $1.50 |
| 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-3.5 Turbo 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 2.5x cheaper ($0.60 vs $1.50 per million) and a larger context window (128K against 16K tokens).
Which is cheaper, Command R (08-2024) or GPT-3.5 Turbo?
Command R (08-2024) costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-3.5 Turbo costs $0.50 and $1.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R (08-2024) or GPT-3.5 Turbo?
Command R (08-2024), with 128K tokens against 16K.
Has any benchmark tested both Command R (08-2024) and GPT-3.5 Turbo?
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-3.5 Turbo on AA Coding Index, SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 22, 2026.