Command R7B (12-2024) vs GPT-5.2 Pro
Command R7B (12-2024) costs 1120.0x less per output token than GPT-5.2 Pro ($0.15 vs $168.00 per million). Updated October 21, 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
- $168.00
Command R7B (12-2024) or GPT-5.2 Pro: 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 1120.0x cheaper ($0.15 vs $168.00 per million).
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GPT-5.2 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. 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
Long documents
Listed context window
GPT-5.2 Pro
GPT-5.2 Pro takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R7B (12-2024)
Command R7B (12-2024) $0.0001, GPT-5.2 Pro $0.105 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, GPT-5.2 Pro 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-5.2 Pro $168.00 per million output tokens, 1120.0x apart.
- Context window
- Command R7B (12-2024) takes 128K tokens, GPT-5.2 Pro 400K.
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.
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Command R7B (12-2024) | GPT-5.2 Pro | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 156.3 | Only GPT-5.2 Pro 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-5.2 Pro | 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 index4 boards measured
| Benchmark | Command R7B (12-2024) | GPT-5.2 Pro | Leader |
|---|---|---|---|
| ARC-AGI | Not measured | 90.5% | Only GPT-5.2 Pro measured |
| ARC-AGI-2 | Not measured | 54.2% | Only GPT-5.2 Pro measured |
| SimpleBench | Not measured | 57.4% | Only GPT-5.2 Pro measured |
| Chess puzzles | Not measured | 60.0% | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | GPT-5.2 Pro | Leader |
|---|---|---|---|
| FrontierMath, tiers 1 to 3 | Not measured | 74.0% | Only GPT-5.2 Pro measured |
| FrontierMath, tier 4 | Not measured | 46.0% | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 11 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 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-5.2 Pro |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 14, 2024 | December 10, 2025 |
| Context window | 128K | 400K |
| Input $/Mtok | $0.0375 | $21.00 |
| Output $/Mtok | $0.15 | $168.00 |
| 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 R7B (12-2024) better than GPT-5.2 Pro 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 1120.0x cheaper ($0.15 vs $168.00 per million).
Which is cheaper, Command R7B (12-2024) or GPT-5.2 Pro?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; GPT-5.2 Pro costs $21.00 and $168.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R7B (12-2024) or GPT-5.2 Pro?
GPT-5.2 Pro, with 400K tokens against 128K.
Has any benchmark tested both Command R7B (12-2024) and GPT-5.2 Pro?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GPT-5.2 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. Compare them on price and context instead. Data as of October 21, 2026.