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Model comparison

Claude Opus 4.6 vs GPT-OSS 20B

Data verified

Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

68.59/100
Margin
25.9pts
← winning
42.74/100
0 category wins0 category wins

Public leaderboard positions: Claude Opus 4.6 #16 (Supported); GPT-OSS 20B #159 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.6 and GPT-OSS 20B share 13 comparable benchmark results. 0 of 8 categories are comparable. 33 results are unique to Claude Opus 4.6; 5 to GPT-OSS 20B.

Updated July 20, 2026
Shared results
13
Claude Opus 4.6 only
33
GPT-OSS 20B only
5
Comparable categories
0 / 8

Benchmark data for Claude Opus 4.6 and GPT-OSS 20B is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

Claude Opus 4.6 is priced at $5.00 input / $25.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GPT-OSS 20B. Claude Opus 4.6 has the larger context window at 1M, compared with 128K for GPT-OSS 20B.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Claude Opus 4.6 and GPT-OSS 20B
CategoryClaude Opus 4.6ΔGPT-OSS 20B
AgenticClaude Opus 4.673.0MarginNo overlapGPT-OSS 20BNot measured
CodingClaude Opus 4.668.1MarginNo overlapGPT-OSS 20BNot measured
KnowledgeClaude Opus 4.669.1MarginNo overlapGPT-OSS 20BNot measured
MathClaude Opus 4.636.3MarginNo overlapGPT-OSS 20BNot measured
MultimodalClaude Opus 4.677.3MarginNo overlapGPT-OSS 20BNot measured

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.6GPT-OSS 20BComparison
Input / output priceUSD per 1M tokensClaude Opus 4.6$5 input / $25 outputGPT-OSS 20B$0 input / $0 outputGPT-OSS 20B has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.640 tok/sGPT-OSS 20B313 tok/sGPT-OSS 20B has the higher measured throughput.
First-answer latencyseconds to first tokenClaude Opus 4.61.78 sGPT-OSS 20B0.65 sGPT-OSS 20B reaches the first token sooner.
Context windowmaximum listed tokensClaude Opus 4.61MGPT-OSS 20B128KClaude Opus 4.6 lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
Terminal-Bench 2.0Source 65.4%—Not comparable
BrowseCompSource 83.7%—Not comparable
OSWorld-VerifiedSource 72.7%—Not comparable
τ²-bench resultsSource 84.8%60.2%Claude Opus 4.6 leads
Claw-EvalSource 70.4%—Not comparable
DeepSearchQASource 73.7%—Not comparable
CyberGymSource 66.6%—Not comparable
Gert LabsSource 61.85%—Not comparable
ResearchClawBenchSource 19.9%—Not comparable
JobBenchSource 36.7%—Not comparable
AA Agentic IndexSource —3.1%Not comparable
APEX-Agents-AASource —0.7%Not comparable
GDPval-AASource —3.0%Not comparable
GDPval-AASource —559Not comparable
Coding
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
SWE-bench VerifiedSource 80.8%—Not comparable
SWE-bench Verified*Source 75.6%—Not comparable
LiveCodeBench ProSource 70.7%—Not comparable
SWE-bench ProSource 53.4%—Not comparable
SWE-RebenchSource 65.3%—Not comparable
React Native EvalsSource 84.1%71%Claude Opus 4.6 leads
Vibe Code BenchSource 57.57%—Not comparable
AA-SciCodeSource 45.7%34.4%Claude Opus 4.6 leads
FrontierCode 1.1 MainSource 26.9%—Not comparable
AA Coding IndexSource —20.7%Not comparable
Reasoning
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
AA-LCRSource 58.3%30.7%Claude Opus 4.6 leads
CritPtSource 2.8%1.4%Claude Opus 4.6 leads
Knowledge
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
GPQASource 91.3%—Not comparable
GPQA-DSource 89.2%—Not comparable
SuperGPQASource 95%—Not comparable
MMLU-ProSource 82%—Not comparable
MMLU-Pro (Arcee)Source 89.1%—Not comparable
HLESource 53%—Not comparable
HLE w/o toolsSource 40%—Not comparable
HealthBench HardSource 14.8%—Not comparable
MedXpertQA (Text)Source 52.1%—Not comparable
Artificial Analysis Intelligence IndexSource 37.8%14.9%Claude Opus 4.6 leads
AA-GPQA DiamondSource 84.0%68.8%Claude Opus 4.6 leads
AA-HLESource 18.6%9.8%Claude Opus 4.6 leads
AA-Omniscience IndexSource 3.5%-63.9%Claude Opus 4.6 leads
AA-Omniscience AccuracySource 45.2%15.5%Claude Opus 4.6 leads
AA-Omniscience Hallucination RateSource 76.0%94.1%Claude Opus 4.6 leads
Math
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
AIME25 (Arcee)Source 99.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 40.700%—Not comparable
FrontierMath v2 (Tier 4)Source 22.900%—Not comparable
Multimodal
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
MMMU-ProSource 77.3%—Not comparable
ERQASource 51.6%—Not comparable
ScreenSpot ProSource 83.1%—Not comparable
MedXpertQA (MM)Source 64.8%—Not comparable
AA-MMMU-ProSource 72.5%—Not comparable
Design Arena WebsiteSource 1328885Claude Opus 4.6 leads
Inst. Following
BenchmarkClaude Opus 4.6GPT-OSS 20BResult
AA-IFBenchSource 44.6%65.1%GPT-OSS 20B leads
Frequently Asked Questions (3)

Can I compare Claude Opus 4.6 and GPT-OSS 20B on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for Claude Opus 4.6 and GPT-OSS 20B today?

Claude Opus 4.6: $5.00 input / $25.00 output per 1M tokens GPT-OSS 20B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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