LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

R1 0528 vs gpt-oss-120b

Updated October 21, 2026. 54 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace R1 0528 Replace gpt-oss-120b
R1 0528DeepSeek
Coding
71.4
Overall
13.1
Output $/M
$2.15
44
gpt-oss-120bOpenAI
Coding
34.5
Overall
11.6
Output $/M
$0.60
10
vs boards won of 54 shared

Decision reading

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 34.5 for gpt-oss-120b, though on 1 benchmark against 2.

Pick gpt-oss-120b when output tokens cost 3.6x less, or when what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AR1 0528DeepSeek71.4/1001 independent benchmarkNo cost per solved taskModel Bgpt-oss-120bOpenAI34.5/1002 independent benchmarks$0.51 per solved task
Shared results
54
R1 0528 only
5
gpt-oss-120b only
24
Like-for-like categories
8 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
R1 0528 71.4 from 1 benchmark, gpt-oss-120b 34.5 from 2 benchmarks.
Widest category
Web design, 21.7 points apart on average over 6 shared benchmarks.
Widest single gap
Fiction.LiveBench: R1 0528 75.0% against gpt-oss-120b 44.4%, 30.6 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.15 against gpt-oss-120b $0.60 per million output tokens, 3.6x apart.
Context window
R1 0528 takes 164K 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
CategoryR1 0528Barsgpt-oss-120bTies
Coding43
Intelligence10
Agentic work02
Reasoning61
Math23
Knowledge50
Web design60
General201
All shared boards4410
CodingLike-for-likeFour of these boards feed the Vibe Coding Index43
Coding benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
Aider polyglot71.4%41.8%R1 0528
SWE-bench VerifiedNot measured26.0%Only gpt-oss-120b measured
AA Coding IndexNot measured30.4Only gpt-oss-120b measured
LiveCodeBench (Vals AI)Not measured83.2%Only gpt-oss-120b measured
SWE-bench Verified (Vals AI)Not measured33.6%Only gpt-oss-120b measured
LiveCodeBench (Artificial Analysis run)77.0%87.8%gpt-oss-120b
SciCodeNot measured34.0%Only gpt-oss-120b measured
Terminal-Bench Hard (Artificial Analysis run)15.9%23.5%gpt-oss-120b
Terminal-BenchNot measured18.7%Only gpt-oss-120b measured
WeirdML41.6%48.2%gpt-oss-120b
ALE-Bench804.1575.6R1 0528
LMArena Text: coding14271379R1 0528
LMArena Text: software and IT14351387R1 0528
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
AA Intelligence Index13.111.6R1 0528
Epoch Capabilities IndexNot measured139.8Only gpt-oss-120b measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
AA Agentic IndexNot measured3.7Only gpt-oss-120b measured
τ²-Bench Telecom36.5%65.8%gpt-oss-120b
APEX-AgentsNot measured4.4%Only gpt-oss-120b measured
METR time horizons53.8%56.6%gpt-oss-120b
DeepResearch Bench35.1%Not measuredOnly R1 0528 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 index61
Reasoning benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
GPQA DiamondNot measured78.5%Only gpt-oss-120b measured
GPQA Diamond (Artificial Analysis run)81.3%78.2%R1 0528
Humanity's Last Exam (Artificial Analysis run)15.8%19.6%gpt-oss-120b
GPQA Diamond (Epoch AI run)76.3%75.8%R1 0528
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%22.1%R1 0528
Mystery game puzzlesNot measured2.0%Only gpt-oss-120b measured
LMArena Text: hard prompts14151364R1 0528
LMArena Text: expert13941356R1 0528
LMArena Text: hard prompts in English14221368R1 0528
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index23
Math benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
AIMENot measured92.6%Only gpt-oss-120b measured
MATH 500 (Vals AI)Not measured94.8%Only gpt-oss-120b measured
MGSM (Vals AI)Not measured92.0%Only gpt-oss-120b measured
AA Math Index76.093.4gpt-oss-120b
AIME 2025 (Artificial Analysis run)76.0%93.4%gpt-oss-120b
OTIS Mock AIME66.4%88.9%gpt-oss-120b
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001389R1 0528
LMArena Text: mathematical work14151386R1 0528
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
MMLU ProNot measured79.2%Only gpt-oss-120b measured
LegalBench (Vals AI)Not measured75.9%Only gpt-oss-120b measured
CaseLaw (Vals AI)Not measured48.8%Only gpt-oss-120b measured
CorpFin (Vals AI)Not measured58.2%Only gpt-oss-120b measured
TaxEval (Vals AI)Not measured71.6%Only gpt-oss-120b measured
MedQA (Vals AI)Not measured91.4%Only gpt-oss-120b measured
MMLU Pro (Artificial Analysis run)84.9%80.8%R1 0528
LMArena Text: science14301382R1 0528
LMArena Text: medicine and healthcare14451388R1 0528
LMArena Text: legal and government14191355R1 0528
LMArena Text: business and finance14021359R1 0528
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index60
Web design benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
Design Arena: websites1149968R1 0528
Design Arena: UI components1102927R1 0528
Design Arena: game dev1098992R1 0528
Design Arena: data viz1182987R1 0528
Design Arena: 3D1111901R1 0528
Design Arena: all code categories1139963R1 0528
Design Arena: SVG1038Not measuredOnly R1 0528 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index201
General benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-oss-120bLeader
IFBench39.6%69.0%gpt-oss-120b
Long context reasoning (AA-LCR)55.7%52.0%R1 0528
DTBenchNot measured76.3%Only gpt-oss-120b measured
LMCANot measured22.1%Only gpt-oss-120b measured
Fiction.LiveBench75.0%44.4%R1 0528
Creative writing (Lech Mazur)81.9%77.1%R1 0528
Surface Evolver BenchNot measured25.0%Only gpt-oss-120b measured
LMArena Text14281366R1 0528
LMArena Text: instruction following13821318R1 0528
LMArena Text: creative writing14051274R1 0528
LMArena Text: English14371375R1 0528
LMArena Text: non-English14121351R1 0528
LMArena Text: Chinese14421385R1 0528
LMArena Text: German14041353R1 0528
LMArena Text: Japanese13911331R1 0528
LMArena Text: Korean13601282R1 0528
LMArena Text: French14171370R1 0528
LMArena Text: Spanish14111389R1 0528
LMArena Text: Russian14231343R1 0528
LMArena Text: Polish14281353R1 0528
LMArena Text: multi-turn14051340R1 0528
LMArena Text: longer queries13911319R1 0528
LMArena Text: writing and language14021306R1 0528
LMArena Text: entertainment and media14001292R1 0528
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and gpt-oss-120b
BenchmarkR1 0528gpt-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

R1 0528$0.0016Fits in one request
gpt-oss-120b$0.0004Fits in one request

gpt-oss-120b costs less

Repository review

50K input + 3K output tokens

R1 0528$0.031Fits in one request
gpt-oss-120b$0.0093Fits in one request

gpt-oss-120b costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.121Exceeds the context window
gpt-oss-120b$0.036Exceeds 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 R1 0528 and gpt-oss-120b
SpecificationDeepSeek R1 0528OpenAI gpt-oss-120b
MakerDeepSeekOpenAI
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025August 5, 2025
Context window164K131K
Input $/Mtok$0.50$0.15
Output $/Mtok$2.15$0.60
Coding index71.434.5
Independent measurements behind it12
Cost per solved taskNot computable$0.51
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than gpt-oss-120b for coding?

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 34.5 for gpt-oss-120b, though on 1 benchmark against 2. Pick gpt-oss-120b when output tokens cost 3.6x less, or when what matters is LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, R1 0528 or gpt-oss-120b?

R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; gpt-oss-120b costs $0.15 and $0.60. Cost per solved task is known for gpt-oss-120b only.

Which has the bigger context window, R1 0528 or gpt-oss-120b?

R1 0528, with 164K tokens against 131K.

How many benchmarks is the R1 0528 vs gpt-oss-120b comparison based on?

54 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and gpt-oss-120b's on 2. Where only one model has a result, the page says so instead of naming a winner.

Compare next