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Command A vs gpt-oss-120b

Updated October 21, 2026. 56 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Command A Replace gpt-oss-120b
Command ACohere
Coding
No data
Overall
13.1
Output $/M
$10.00
13
gpt-oss-120bOpenAI
Coding
No data
Overall
11.6
Output $/M
$0.50
43
vs boards won of 56 shared

Decision reading

Neither model has a coding score yet, so the two cannot be ranked on coding.

Command A has the edge where the context window is larger, 256K against 131K tokens, and where what matters is AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Model ACommand ACohereNot benchmarked1 independent benchmark$2.69 per solved taskModel Bgpt-oss-120bOpenAINot benchmarked1 independent benchmark$0.51 per solved task
Shared results
56
Command A only
4
gpt-oss-120b only
22
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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.

At a glance

Coding index
Command A not benchmarked, gpt-oss-120b not benchmarked.
Widest category
Math, 41.7 points apart on average over 7 shared benchmarks.
Widest single gap
AIME 2025 (Artificial Analysis run): Command A 13.0% against gpt-oss-120b 93.4%, 80.4 points apart on a 0 to 100 scale.
Output price
Command A $10.00 against gpt-oss-120b $0.50 per million output tokens, 20.0x apart.
Context window
Command A takes 256K tokens, gpt-oss-120b 131K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryCommand ABarsgpt-oss-120bTies
Coding26
Intelligence10
Agentic work11
Reasoning06
Math07
Knowledge29
General714
All shared boards1343
CodingLike-for-likeSix of these boards feed the Vibe Coding Index26
Coding benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
Aider polyglotNot measured41.8%Only gpt-oss-120b measured
SWE-bench VerifiedNot measured26.0%Only gpt-oss-120b measured
AA Coding Index27.830.4gpt-oss-120b
LiveCodeBench (Vals AI)35.1%83.2%gpt-oss-120b
SWE-bench Verified (Vals AI)7.8%33.6%gpt-oss-120b
Terminal-Bench 2.1 (Vals AI)17.6%Not measuredOnly Command A measured
LiveCodeBench (Artificial Analysis run)28.7%87.8%gpt-oss-120b
SciCode38.5%34.0%Command A
Terminal-Bench Hard (Artificial Analysis run)25.0%23.5%Command A
Terminal-BenchNot measured18.7%Only gpt-oss-120b measured
WeirdMLNot measured48.2%Only gpt-oss-120b measured
ALE-BenchNot measured575.6Only gpt-oss-120b measured
LMArena Text: coding13301379gpt-oss-120b
LMArena Text: software and IT13361387gpt-oss-120b
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
AA Intelligence Index13.111.6Command A
Epoch Capabilities IndexNot measured139.8Only gpt-oss-120b measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
Agentic work benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
AA Agentic Index1.03.7gpt-oss-120b
Finance Agent (Vals AI)9.0%Not measuredOnly Command A measured
τ²-Bench Telecom80.7%65.8%Command A
APEX-AgentsNot measured4.4%Only gpt-oss-120b measured
METR time horizonsNot measured56.6%Only gpt-oss-120b measured
Vending-Bench 2Not measured-$22Only gpt-oss-120b measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
GPQA Diamond48.5%78.5%gpt-oss-120b
GPQA Diamond (Artificial Analysis run)76.1%78.2%gpt-oss-120b
Humanity's Last Exam (Artificial Analysis run)12.0%19.6%gpt-oss-120b
GPQA Diamond (Epoch AI run)Not measured75.8%Only gpt-oss-120b measured
SimpleBenchNot measured22.1%Only gpt-oss-120b measured
Mystery game puzzlesNot measured2.0%Only gpt-oss-120b measured
LMArena Text: hard prompts13261364gpt-oss-120b
LMArena Text: expert12961356gpt-oss-120b
LMArena Text: hard prompts in English13351368gpt-oss-120b
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index07
Math benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
AIME13.3%92.6%gpt-oss-120b
MATH 500 (Vals AI)76.2%94.8%gpt-oss-120b
MGSM (Vals AI)85.7%92.0%gpt-oss-120b
AA Math Index13.093.4gpt-oss-120b
AIME 2025 (Artificial Analysis run)13.0%93.4%gpt-oss-120b
OTIS Mock AIMENot measured88.9%Only gpt-oss-120b measured
LMArena Text: math13001389gpt-oss-120b
LMArena Text: mathematical work13121386gpt-oss-120b
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index29
Knowledge benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
MMLU Pro69.2%79.2%gpt-oss-120b
LegalBench (Vals AI)79.7%75.9%Command A
CaseLaw (Vals AI)64.5%48.8%Command A
CorpFin (Vals AI)46.0%58.2%gpt-oss-120b
TaxEval (Vals AI)61.4%71.6%gpt-oss-120b
MedQA (Vals AI)80.5%91.4%gpt-oss-120b
MedCode (Vals AI)19.7%Not measuredOnly Command A measured
MMLU Pro (Artificial Analysis run)71.2%80.8%gpt-oss-120b
LMArena Text: science13391382gpt-oss-120b
LMArena Text: medicine and healthcare13341388gpt-oss-120b
LMArena Text: legal and government13481355gpt-oss-120b
LMArena Text: business and finance13151359gpt-oss-120b
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
Design Arena: websitesNot measured968Only gpt-oss-120b measured
Design Arena: UI componentsNot measured927Only gpt-oss-120b measured
Design Arena: game devNot measured992Only gpt-oss-120b measured
Design Arena: data vizNot measured987Only gpt-oss-120b measured
Design Arena: 3DNot measured901Only gpt-oss-120b measured
Design Arena: all code categoriesNot measured963Only gpt-oss-120b measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index714
General benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
MedScribe (Vals AI)55.7%Not measuredOnly Command A measured
IFBench73.9%69.0%Command A
Long context reasoning (AA-LCR)52.7%52.0%Command A
DTBench61.3%76.3%gpt-oss-120b
LMCA10.3%22.1%gpt-oss-120b
Fiction.LiveBenchNot measured44.4%Only gpt-oss-120b measured
Creative writing (Lech Mazur)Not measured77.1%Only gpt-oss-120b measured
Surface Evolver BenchNot measured25.0%Only gpt-oss-120b measured
LMArena Text13311366gpt-oss-120b
LMArena Text: instruction following13091318gpt-oss-120b
LMArena Text: creative writing13191274Command A
LMArena Text: English13441375gpt-oss-120b
LMArena Text: non-English13131351gpt-oss-120b
LMArena Text: Chinese13271385gpt-oss-120b
LMArena Text: German13421353gpt-oss-120b
LMArena Text: Japanese12851331gpt-oss-120b
LMArena Text: Korean12851282Command A
LMArena Text: French13511370gpt-oss-120b
LMArena Text: Spanish13471389gpt-oss-120b
LMArena Text: Russian13141343gpt-oss-120b
LMArena Text: Polish13241353gpt-oss-120b
LMArena Text: multi-turn13381340gpt-oss-120b
LMArena Text: longer queries13341319Command A
LMArena Text: writing and language13281306Command A
LMArena Text: entertainment and media13061292Command A
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Command A and gpt-oss-120b
BenchmarkCommand Agpt-oss-120bLeader
Neither model has a result on 5 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 A$0.0075Fits in one request
gpt-oss-120b$0.0003Fits in one request

gpt-oss-120b costs less

Repository review

50K input + 3K output tokens

Command A$0.155Fits in one request
gpt-oss-120b$0.0065Fits in one request

gpt-oss-120b costs less

Agent loop

200K input + 10K output tokens

Command A$0.600Fits in one request
gpt-oss-120b$0.025Exceeds the context window

gpt-oss-120b 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.

Specifications of Command A and gpt-oss-120b
SpecificationCohere Command AOpenAI gpt-oss-120b
MakerCohereOpenAI
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025August 5, 2025
Context window256K131K
Input $/Mtok$2.50$0.10
Output $/Mtok$10.00$0.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it11
Cost per solved task$2.69$0.51
SourceOpenRouter listingOpenRouter listing

Questions

Is Command A better than gpt-oss-120b for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Command A has the edge where the context window is larger, 256K against 131K tokens, and where what matters is AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, Command A or gpt-oss-120b?

Command A costs $2.50 per million input tokens and $10.00 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Per solved SWE-bench Verified task, Command A costs $2.69 and gpt-oss-120b $0.51.

Which has the bigger context window, Command A or gpt-oss-120b?

Command A, with 256K tokens against 131K.

How many benchmarks is the Command A vs gpt-oss-120b comparison based on?

56 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.

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