Command R7B (12-2024) vs GPT-3.5 Turbo
Command R7B (12-2024) costs 10.0x less per output token than GPT-3.5 Turbo ($0.15 vs $1.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
Command R7B (12-2024) 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 R7B (12-2024) for output tokens 10.0x cheaper ($0.15 vs $1.50 per million) and a larger context window (128K against 16K tokens).
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); 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 R7B (12-2024)
Command R7B (12-2024) takes 128K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R7B (12-2024)
Command R7B (12-2024) $0.0001, GPT-3.5 Turbo $0.0013 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) 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 R7B (12-2024) $0.15 against GPT-3.5 Turbo $1.50 per million output tokens, 10.0x apart.
- Context window
- Command R7B (12-2024) 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 Index2 boards measured
| Benchmark | Command R7B (12-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 10.7 | Only GPT-3.5 Turbo measured |
| SciCode | Not measured | 0.0% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Command R7B (12-2024) | 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 | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Command R7B (12-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | Not measured | Only Command R7B (12-2024) measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Command R7B (12-2024) | 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 |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Command R7B (12-2024) | GPT-3.5 Turbo | Leader |
|---|---|---|---|
| GSM8K | Not measured | 57.8% | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Command R7B (12-2024) | 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 |
| Neither model has a result on the other 14 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
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 R7B (12-2024) costs less
Repository review
50K input + 3K output tokens
Command R7B (12-2024) costs less
Agent loop
200K input + 10K output tokens
Command R7B (12-2024) 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 R7B (12-2024) | OpenAI GPT-3.5 Turbo |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 14, 2024 | May 28, 2023 |
| Context window | 128K | 16K |
| Input $/Mtok | $0.0375 | $0.50 |
| Output $/Mtok | $0.15 | $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 R7B (12-2024) 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 R7B (12-2024) for output tokens 10.0x cheaper ($0.15 vs $1.50 per million) and a larger context window (128K against 16K tokens).
Which is cheaper, Command R7B (12-2024) or GPT-3.5 Turbo?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 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 R7B (12-2024) or GPT-3.5 Turbo?
Command R7B (12-2024), with 128K tokens against 16K.
Has any benchmark tested both Command R7B (12-2024) and GPT-3.5 Turbo?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); 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.