Model profile
OpenAI

OpenAI: gpt-oss-20b

OpenAI: gpt-oss-20b is a budget-priced text-first model from OpenAI with fast runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyExtended contextBudget pricing
Intelligence
20.8

Benchmark blend

Coding
14.4

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.06

Budget tier

Decision snapshot
53

OpenAI: gpt-oss-20b currently reads as a budget text-first option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
20.8
21

General reasoning and benchmark headroom.

Limited
Speed
234 tok/s
98

TTFT 0.47s

Above average
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.06
86

$0.20 output / 1M

Efficient

Editorial Profile

OpenAI: gpt-oss-20b in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 14Math score 62

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...

Identity

OpenAI text-first profile

Positioning

Long-context research / Agent workflows with extended context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
20.8
MMLU Pro
71.8%
GPQA
61.1%
HLE
5.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
14.4
LiveCodeBench
0.652
SciCode
34.0%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
62.3
AIME 2025
62.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
57.8%
TAU2
50.3%
TerminalBench Hard
4.5%
LCR
31.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
131K Tokens
Vision
Text-first
Modalities
text
Tokenizer
GPT
Max Completion
131072
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningreasoning_effortrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.06
Output
per 1M output tokens
$0.20
Blended
AA 3:1 mix
$0.09

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.