Command R7B (12-2024) vs gpt-oss-20b
Command R7B (12-2024) and gpt-oss-20b cost about the same per output token ($0.15 vs $0.15 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
- 38.5
- Output $/M
- $0.15
Command R7B (12-2024) or gpt-oss-20b: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); gpt-oss-20b on LiveCodeBench (Vals AI), LiveCodeBench (Artificial Analysis run) and SciCode. 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-oss-20b has a AA Intelligence Index result, 10.0.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-20b has a GPQA Diamond result, 68.9%.
Directional only
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-20b has a MMLU Pro result, 71.6%.
Directional only
Web design
Design Arena: websites
No call
Only gpt-oss-20b has a Design Arena: websites result, 852.
Directional only
Long documents
Listed context window
gpt-oss-20b
gpt-oss-20b takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
Command R7B (12-2024) $0.0001, gpt-oss-20b $0.0001 per turn.
List prices
At a glance
- Coding index
- Command R7B (12-2024) not benchmarked, gpt-oss-20b 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
- Both cost $0.15 per million output tokens.
- Context window
- Command R7B (12-2024) takes 128K tokens, gpt-oss-20b 131K.
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 R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 80.4% | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 77.7% | Only gpt-oss-20b measured |
| SciCode | Not measured | 38.9% | Only gpt-oss-20b measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 10.6% | Only gpt-oss-20b measured |
| Terminal-Bench | Not measured | 3.4% | Only gpt-oss-20b measured |
| WeirdML | Not measured | 40.9% | Only gpt-oss-20b measured |
| ALE-Bench | Not measured | 566.0 | Only gpt-oss-20b measured |
| LMArena Text: coding | Not measured | 1307 | Only gpt-oss-20b measured |
| LMArena Text: software and IT | Not measured | 1311 | Only gpt-oss-20b measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 10.0 | Only gpt-oss-20b measured |
| Epoch Capabilities Index | Not measured | 137.7 | Only gpt-oss-20b measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 60.2% | Only gpt-oss-20b measured |
| Berkeley Function Calling Leaderboard (BFCL) | 32.1% | Not measured | Only Command R7B (12-2024) measured |
| BrowseComp-Plus | Not measured | 50.6% | Only gpt-oss-20b measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 68.9% | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 68.8% | Only gpt-oss-20b measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 11.0% | Only gpt-oss-20b measured |
| GPQA Diamond (Epoch AI run) | Not measured | 60.8% | Only gpt-oss-20b measured |
| LMArena Text: hard prompts | Not measured | 1274 | Only gpt-oss-20b measured |
| LMArena Text: expert | Not measured | 1258 | Only gpt-oss-20b measured |
| LMArena Text: hard prompts in English | Not measured | 1292 | Only gpt-oss-20b measured |
| Kagi LLM Benchmark | Not measured | 53.2% | Only gpt-oss-20b measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| AIME | Not measured | 86.0% | Only gpt-oss-20b measured |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only gpt-oss-20b measured |
| MGSM (Vals AI) | Not measured | 89.0% | Only gpt-oss-20b measured |
| AA Math Index | Not measured | 89.3 | Only gpt-oss-20b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 89.3% | Only gpt-oss-20b measured |
| OTIS Mock AIME | Not measured | 65.3% | Only gpt-oss-20b measured |
| LMArena Text: math | Not measured | 1317 | Only gpt-oss-20b measured |
| LMArena Text: mathematical work | Not measured | 1305 | Only gpt-oss-20b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 71.6% | Only gpt-oss-20b measured |
| LegalBench (Vals AI) | Not measured | 70.8% | Only gpt-oss-20b measured |
| CaseLaw (Vals AI) | Not measured | 43.8% | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | Not measured | 53.1% | Only gpt-oss-20b measured |
| TaxEval (Vals AI) | Not measured | 63.7% | Only gpt-oss-20b measured |
| MedQA (Vals AI) | Not measured | 82.9% | Only gpt-oss-20b measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 74.8% | Only gpt-oss-20b measured |
| LMArena Text: science | Not measured | 1296 | Only gpt-oss-20b measured |
| LMArena Text: medicine and healthcare | Not measured | 1310 | Only gpt-oss-20b measured |
| LMArena Text: legal and government | Not measured | 1280 | Only gpt-oss-20b measured |
| LMArena Text: business and finance | Not measured | 1284 | Only gpt-oss-20b measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 852 | Only gpt-oss-20b measured |
| Design Arena: data viz | Not measured | 933 | Only gpt-oss-20b measured |
| Neither model has a result on the other 7 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards measured
| Benchmark | Command R7B (12-2024) | gpt-oss-20b | Leader |
|---|---|---|---|
| IFBench | Not measured | 65.1% | Only gpt-oss-20b measured |
| Long context reasoning (AA-LCR) | Not measured | 34.7% | Only gpt-oss-20b measured |
| DTBench | Not measured | 68.0% | Only gpt-oss-20b measured |
| LMCA | Not measured | 14.5% | Only gpt-oss-20b measured |
| LMArena Text | Not measured | 1288 | Only gpt-oss-20b measured |
| LMArena Text: instruction following | Not measured | 1236 | Only gpt-oss-20b measured |
| LMArena Text: creative writing | Not measured | 1201 | Only gpt-oss-20b measured |
| LMArena Text: English | Not measured | 1303 | Only gpt-oss-20b measured |
| LMArena Text: non-English | Not measured | 1269 | Only gpt-oss-20b measured |
| LMArena Text: Chinese | Not measured | 1315 | Only gpt-oss-20b measured |
| LMArena Text: German | Not measured | 1255 | Only gpt-oss-20b measured |
| LMArena Text: Japanese | Not measured | 1244 | Only gpt-oss-20b measured |
| LMArena Text: Korean | Not measured | 1236 | Only gpt-oss-20b measured |
| LMArena Text: Spanish | Not measured | 1267 | Only gpt-oss-20b measured |
| LMArena Text: Russian | Not measured | 1278 | Only gpt-oss-20b measured |
| LMArena Text: Polish | Not measured | 1252 | Only gpt-oss-20b measured |
| LMArena Text: multi-turn | Not measured | 1268 | Only gpt-oss-20b measured |
| LMArena Text: longer queries | Not measured | 1250 | Only gpt-oss-20b measured |
| LMArena Text: writing and language | Not measured | 1238 | Only gpt-oss-20b measured |
| LMArena Text: entertainment and media | Not measured | 1216 | Only gpt-oss-20b measured |
| Neither model has a result on the other 26 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-oss-20b costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-20b 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-oss-20b |
|---|---|---|
| Maker | Cohere | OpenAI |
| Weights | No public weights listed | Open weights |
| Released | December 14, 2024 | August 5, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $0.0375 | $0.03 |
| Output $/Mtok | $0.15 | $0.15 |
| 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-oss-20b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Command R7B (12-2024) or gpt-oss-20b?
Command R7B (12-2024) costs $0.0375 per million input tokens and $0.15 per million output tokens; gpt-oss-20b costs $0.03 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Command R7B (12-2024) or gpt-oss-20b?
gpt-oss-20b, with 131K tokens against 128K.
Has any benchmark tested both Command R7B (12-2024) and gpt-oss-20b?
No benchmark has tested both yet. Command R7B (12-2024) has results on Berkeley Function Calling Leaderboard (BFCL); gpt-oss-20b on LiveCodeBench (Vals AI), LiveCodeBench (Artificial Analysis run) and SciCode. Compare them on price and context instead. Data as of October 21, 2026.