Command R7B (12-2024) vs GPT-6 Sol
Command R7B (12-2024) costs 66.7x less per output token than GPT-6 Sol ($0.15 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- 76.0
- Overall
- 74.6
- Output $/M
- $10.00
Command R7B (12-2024) or GPT-6 Sol: which to pick
Only GPT-6 Sol has a coding score, 76.0 on the Vibe Coding Index; Command R7B (12-2024) has none, so the two cannot be ranked on coding.
Pick Command R7B (12-2024) for output tokens 66.7x cheaper ($0.15 vs $10.00 per million).
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GPT-6 Sol on LiveBench coding, LMArena WebDev and Terminal-Bench 4 (Vals AI). 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
Only GPT-6 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-6 Sol has a AA Intelligence Index result, 47.6.
Directional only
Web design
Design Arena: websites
No call
Only GPT-6 Sol has a Design Arena: websites result, 1292.
Directional only
Long documents
Listed context window
GPT-6 Sol
GPT-6 Sol takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Command R7B (12-2024)
Command R7B (12-2024) $0.0001, GPT-6 Sol $0.0070 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, GPT-6 Sol 76.0 from 5 benchmarks.
- 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-6 Sol $10.00 per million output tokens, 66.7x apart.
- Context window
- Command R7B (12-2024) takes 128K tokens, GPT-6 Sol 1.05M.
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 Index17 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 93.1% | Only GPT-6 Sol measured |
| LMArena WebDev | Not measured | 1683 | Only GPT-6 Sol measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 44.4% | Only GPT-6 Sol measured |
| Vibe Code Bench (Vals AI) | Not measured | 87.8% | Only GPT-6 Sol measured |
| Code migration (Vals AI) | Not measured | 57.2% | Only GPT-6 Sol measured |
| ProgramBench (Vals AI) | Not measured | 2.0% | Only GPT-6 Sol measured |
| IOI (Vals AI) | Not measured | 82.6% | Only GPT-6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 83.2% | Only GPT-6 Sol measured |
| CyberBench (Vals AI) | Not measured | 78.0% | Only GPT-6 Sol measured |
| SciCode | Not measured | 57.6% | Only GPT-6 Sol measured |
| FrontierCode | Not measured | 49.3% | Only GPT-6 Sol measured |
| ALE-Bench | Not measured | 2462.0 | Only GPT-6 Sol measured |
| LMArena Text: coding | Not measured | 1448 | Only GPT-6 Sol measured |
| LMArena Text: software and IT | Not measured | 1433 | Only GPT-6 Sol measured |
| LMArena WebDev: React | Not measured | 1685 | Only GPT-6 Sol measured |
| LMArena WebDev: HTML | Not measured | 1685 | Only GPT-6 Sol measured |
| LMArena WebDev: from an image | Not measured | 1660 | Only GPT-6 Sol measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 47.6 | Only GPT-6 Sol measured |
| Epoch Capabilities Index | Not measured | 163.7 | Only GPT-6 Sol measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1231 | Only GPT-6 Sol measured |
| Design Arena: agents building full-stack apps | Not measured | 1238 | Only GPT-6 Sol measured |
| Design Arena: agents building mobile apps | Not measured | 1214 | Only GPT-6 Sol measured |
| LiveBench research | Not measured | 69.3% | Only GPT-6 Sol measured |
| LiveBench browser | Not measured | 92.0% | Only GPT-6 Sol measured |
| LiveBench long agentic | Not measured | 66.9% | Only GPT-6 Sol measured |
| SREBench (Vals AI) | Not measured | 30.5% | Only GPT-6 Sol measured |
| Finance Agent (Vals AI) | Not measured | 49.0% | Only GPT-6 Sol measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.0% | Only GPT-6 Sol measured |
| Legal Research Bench (Vals AI) | Not measured | 28.9% | Only GPT-6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 71.5% | Only GPT-6 Sol measured |
| BioMysteryBench (Vals AI) | Not measured | 74.8% | Only GPT-6 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 30.0% | Only GPT-6 Sol measured |
| Vals Science Index | Not measured | 45.0% | Only GPT-6 Sol measured |
| APEX-Agents | Not measured | 54.3% | Only GPT-6 Sol measured |
| SpireClimb | Not measured | 7.5% | Only GPT-6 Sol measured |
| Vending-Bench 2 | Not measured | $14,428 | Only GPT-6 Sol measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | Not measured | Only Command R7B (12-2024) measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index14 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 88.7% | Only GPT-6 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 30.2% | Only GPT-6 Sol measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 47.9% | Only GPT-6 Sol measured |
| GPQA Diamond (Epoch AI run) | Not measured | 94.3% | Only GPT-6 Sol measured |
| ARC-AGI | Not measured | 95.5% | Only GPT-6 Sol measured |
| ARC-AGI-2 | Not measured | 89.6% | Only GPT-6 Sol measured |
| Chess puzzles | Not measured | 56.0% | Only GPT-6 Sol measured |
| Mystery game puzzles | Not measured | 56.0% | Only GPT-6 Sol measured |
| EBR-bench | Not measured | 53.3% | Only GPT-6 Sol measured |
| LMArena Text: hard prompts | Not measured | 1422 | Only GPT-6 Sol measured |
| LMArena Text: expert | Not measured | 1437 | Only GPT-6 Sol measured |
| LMArena Text: hard prompts in English | Not measured | 1420 | Only GPT-6 Sol measured |
| LMArena Vision: diagrams | Not measured | 1270 | Only GPT-6 Sol measured |
| LMArena Vision: homework | Not measured | 1286 | Only GPT-6 Sol measured |
| Neither model has a result on the other 8 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 100.0% | Only GPT-6 Sol measured |
| FrontierMath, tiers 1 to 3 | Not measured | 89.8% | Only GPT-6 Sol measured |
| FrontierMath, tier 4 | Not measured | 90.0% | Only GPT-6 Sol measured |
| ProofBench | Not measured | 83.0% | Only GPT-6 Sol measured |
| LMArena Text: math | Not measured | 1407 | Only GPT-6 Sol measured |
| LMArena Text: mathematical work | Not measured | 1424 | Only GPT-6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 47.1% | Only GPT-6 Sol measured |
| SimpleQA Verified | Not measured | 60.7% | Only GPT-6 Sol measured |
| LMArena Text: science | Not measured | 1389 | Only GPT-6 Sol measured |
| LMArena Text: medicine and healthcare | Not measured | 1366 | Only GPT-6 Sol measured |
| LMArena Text: legal and government | Not measured | 1396 | Only GPT-6 Sol measured |
| LMArena Text: business and finance | Not measured | 1380 | Only GPT-6 Sol measured |
| FACTS Benchmark Suite | Not measured | 72.5% | Only GPT-6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1292 | Only GPT-6 Sol measured |
| Design Arena: UI components | Not measured | 1292 | Only GPT-6 Sol measured |
| Design Arena: game dev | Not measured | 1314 | Only GPT-6 Sol measured |
| Design Arena: data viz | Not measured | 1269 | Only GPT-6 Sol measured |
| Design Arena: 3D | Not measured | 1318 | Only GPT-6 Sol measured |
| Design Arena: all code categories | Not measured | 1301 | Only GPT-6 Sol measured |
| Design Arena: SVG | Not measured | 1316 | Only GPT-6 Sol measured |
| Design Arena: ASCII art | Not measured | 1225 | Only GPT-6 Sol measured |
| LiveBench design | Not measured | 82.2% | Only GPT-6 Sol measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index29 boards measured
| Benchmark | Command R7B (12-2024) | GPT-6 Sol | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.2% | Only GPT-6 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 56.6% | Only GPT-6 Sol measured |
| SAGE (Vals AI) | Not measured | 44.8% | Only GPT-6 Sol measured |
| MedScribe (Vals AI) | Not measured | 82.0% | Only GPT-6 Sol measured |
| Long context reasoning (AA-LCR) | Not measured | 83.7% | Only GPT-6 Sol measured |
| DTBench | Not measured | 97.3% | Only GPT-6 Sol measured |
| LMCA | Not measured | 59.1% | Only GPT-6 Sol measured |
| Furniture assembly | Not measured | 58.3% | Only GPT-6 Sol measured |
| LMArena Text | Not measured | 1398 | Only GPT-6 Sol measured |
| LMArena Text: instruction following | Not measured | 1413 | Only GPT-6 Sol measured |
| LMArena Text: creative writing | Not measured | 1378 | Only GPT-6 Sol measured |
| LMArena Vision | Not measured | 1240 | Only GPT-6 Sol measured |
| LMArena Text: English | Not measured | 1396 | Only GPT-6 Sol measured |
| LMArena Text: non-English | Not measured | 1388 | Only GPT-6 Sol measured |
| LMArena Text: Chinese | Not measured | 1412 | Only GPT-6 Sol measured |
| LMArena Text: German | Not measured | 1379 | Only GPT-6 Sol measured |
| LMArena Text: Japanese | Not measured | 1377 | Only GPT-6 Sol measured |
| LMArena Text: Korean | Not measured | 1336 | Only GPT-6 Sol measured |
| LMArena Text: French | Not measured | 1399 | Only GPT-6 Sol measured |
| LMArena Text: Spanish | Not measured | 1387 | Only GPT-6 Sol measured |
| LMArena Text: Russian | Not measured | 1402 | Only GPT-6 Sol measured |
| LMArena Text: Polish | Not measured | 1372 | Only GPT-6 Sol measured |
| LMArena Text: multi-turn | Not measured | 1414 | Only GPT-6 Sol measured |
| LMArena Text: longer queries | Not measured | 1416 | Only GPT-6 Sol measured |
| LMArena Text: writing and language | Not measured | 1389 | Only GPT-6 Sol measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only GPT-6 Sol measured |
| LMArena Vision: English | Not measured | 1229 | Only GPT-6 Sol measured |
| LMArena Vision: reading text | Not measured | 1258 | Only GPT-6 Sol measured |
| LMArena Vision: creative writing | Not measured | 1219 | Only GPT-6 Sol measured |
| Neither model has a result on the other 17 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-6 Sol |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 14, 2024 | September 22, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $0.0375 | $2.00 |
| Output $/Mtok | $0.15 | $10.00 |
| Coding index | Not benchmarked | 76.0 |
| Independent measurements behind it | 0 | 5 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command R7B (12-2024) better than GPT-6 Sol for coding?
Only GPT-6 Sol has a coding score, 76.0 on the Vibe Coding Index; Command R7B (12-2024) has none, so the two cannot be ranked on coding. Pick Command R7B (12-2024) for output tokens 66.7x cheaper ($0.15 vs $10.00 per million).
Which is cheaper, Command R7B (12-2024) or GPT-6 Sol?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; GPT-6 Sol costs $2.00 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R7B (12-2024) or GPT-6 Sol?
GPT-6 Sol, with 1.05M tokens against 128K.
Has any benchmark tested both Command R7B (12-2024) and GPT-6 Sol?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); GPT-6 Sol on LiveBench coding, LMArena WebDev and Terminal-Bench 4 (Vals AI). Compare them on price and context instead. Data as of October 21, 2026.