Command A vs GPT-3.5 Turbo (older v0613)
GPT-3.5 Turbo (older v0613) costs 5.0x less per output token than Command A ($2.00 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 13.1
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
Command A or GPT-3.5 Turbo (older v0613): which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command A for a larger context window (256K against 4K tokens).
No benchmark has tested both yet. Command A has results on AA Coding Index, LiveCodeBench (Vals AI) and SWE-bench Verified (Vals AI); GPT-3.5 Turbo (older v0613) on GSM8K, MMLU and WinoGrande. 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 Command A has a AA Intelligence Index result, 13.1.
Directional only
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Command A has a GPQA Diamond result, 48.5%.
Directional only
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Command A has a MMLU Pro result, 69.2%.
Directional only
Long documents
Listed context window
Command A
Command A takes 256K tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo (older v0613)
Command A $0.0075, GPT-3.5 Turbo (older v0613) $0.0020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Command A has the number, $2.69.
Directional only
At a glance
- Coding index
- Command A not benchmarked, GPT-3.5 Turbo (older v0613) 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 A $10.00 against GPT-3.5 Turbo (older v0613) $2.00 per million output tokens, 5.0x apart.
- Context window
- Command A takes 256K tokens, GPT-3.5 Turbo (older v0613) 4K.
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 Index9 boards measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| SWE-bench Verified (Vals AI) | 7.8% | Not measured | Only Command A measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | Not measured | Only Command A measured |
| LMArena Text: coding | 1330 | Not measured | Only Command A measured |
| LMArena Text: software and IT | 1336 | Not measured | Only Command A measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | Not measured | Only Command A measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | Not measured | Only Command A measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | Not measured | Only Command A measured |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | Not measured | Only Command A measured |
| LMArena Text: hard prompts | 1326 | Not measured | Only Command A measured |
| LMArena Text: expert | 1296 | Not measured | Only Command A measured |
| LMArena Text: hard prompts in English | 1335 | Not measured | Only Command A measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| AIME | 13.3% | Not measured | Only Command A measured |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | Not measured | Only Command A measured |
| AA Math Index | 13.0 | Not measured | Only Command A measured |
| AIME 2025 (Artificial Analysis run) | 13.0% | Not measured | Only Command A measured |
| GSM8K | Not measured | 46.9% | Only GPT-3.5 Turbo (older v0613) measured |
| LMArena Text: math | 1300 | Not measured | Only Command A measured |
| LMArena Text: mathematical work | 1312 | Not measured | Only Command A measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | Not measured | Only Command A measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Command A measured |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | Not measured | Only Command A measured |
| TaxEval (Vals AI) | 61.4% | Not measured | Only Command A measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| MMLU | Not measured | 68.9% | Only GPT-3.5 Turbo (older v0613) measured |
| LMArena Text: science | 1339 | Not measured | Only Command A measured |
| LMArena Text: medicine and healthcare | 1334 | Not measured | Only Command A measured |
| LMArena Text: legal and government | 1348 | Not measured | Only Command A measured |
| LMArena Text: business and finance | 1315 | Not measured | Only Command A measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards measured
| Benchmark | Command A | GPT-3.5 Turbo (older v0613) | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | Not measured | Only Command A measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only Command A measured |
| DTBench | 61.3% | Not measured | Only Command A measured |
| LMCA | 10.3% | Not measured | Only Command A measured |
| WinoGrande | Not measured | 81.6% | Only GPT-3.5 Turbo (older v0613) measured |
| LMArena Text | 1331 | Not measured | Only Command A measured |
| LMArena Text: instruction following | 1309 | Not measured | Only Command A measured |
| LMArena Text: creative writing | 1319 | Not measured | Only Command A measured |
| LMArena Text: English | 1344 | Not measured | Only Command A measured |
| LMArena Text: non-English | 1313 | Not measured | Only Command A measured |
| LMArena Text: Chinese | 1327 | Not measured | Only Command A measured |
| LMArena Text: German | 1342 | Not measured | Only Command A measured |
| LMArena Text: Japanese | 1285 | Not measured | Only Command A measured |
| LMArena Text: Korean | 1285 | Not measured | Only Command A measured |
| LMArena Text: French | 1351 | Not measured | Only Command A measured |
| LMArena Text: Spanish | 1347 | Not measured | Only Command A measured |
| LMArena Text: Russian | 1314 | Not measured | Only Command A measured |
| LMArena Text: Polish | 1324 | Not measured | Only Command A measured |
| LMArena Text: multi-turn | 1338 | Not measured | Only Command A measured |
| LMArena Text: longer queries | 1334 | Not measured | Only Command A measured |
| LMArena Text: writing and language | 1328 | Not measured | Only Command A measured |
| LMArena Text: entertainment and media | 1306 | Not measured | Only Command A measured |
| Neither model has a result on the other 23 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
GPT-3.5 Turbo (older v0613) costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo (older v0613) costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo (older v0613) 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 A | OpenAI GPT-3.5 Turbo (older v0613) |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | January 25, 2024 |
| Context window | 256K | 4K |
| Input $/Mtok | $2.50 | $1.00 |
| Output $/Mtok | $10.00 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $2.69 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than GPT-3.5 Turbo (older v0613) for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command A for a larger context window (256K against 4K tokens).
Which is cheaper, Command A or GPT-3.5 Turbo (older v0613)?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-3.5 Turbo (older v0613) costs $1.00 and $2.00. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or GPT-3.5 Turbo (older v0613)?
Command A, with 256K tokens against 4K.
Has any benchmark tested both Command A and GPT-3.5 Turbo (older v0613)?
No benchmark has tested both yet. Command A has results on AA Coding Index, LiveCodeBench (Vals AI) and SWE-bench Verified (Vals AI); GPT-3.5 Turbo (older v0613) on GSM8K, MMLU and WinoGrande. Compare them on price and context instead. Data as of October 21, 2026.