Llama 3.1 405B Instruct vs gpt-oss-120b

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.037Deepinfra · Sep 21, 2026
Output priceFrom · USD / 1M tokensNot reported$0.17Deepinfra · Sep 21, 2026
Context windowMaximum documented tokens131K131K
Model facts checkedAug 28, 2026View model evidence →Aug 28, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldLlama-3.1-405B-Instructgpt-oss-120b
DeveloperMetaOpenAI
FamilyLlama 3 1 405b InstructGpt Oss 120b
ModelLlama-3.1-405B-Instructgpt-oss-120b
VersionLlama-3.1-405B-Instructgpt-oss-120b
Lifecycleactiveactive
Released2024-07-23Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window131K131K
Total parameters405.9B116.8B
Active parametersUnknown5.1B
Licensellama3.1apache-2.0
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessTogether Ai (Standard)Cerebras (Standard), Deepinfra (Standard), Fireworks Ai (Serverless, Standard), Groq (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Serverless, Standard)
Capabilitieschat, generation, toolschat, generation, reasoning, tools

Llama 3.1 405B Instruct Capabilities

chatgenerationtools
Serving providers1
Canonical IDmeta-llama/Llama-3.1-405B-Instruct

gpt-oss-120b Capabilities

chatgenerationreasoningtools
Serving providers7
Canonical IDopenai/gpt-oss-120b

Primary Evidence

Sources and Freshness

Questions

Llama 3.1 405B Instruct vs gpt-oss-120b FAQs

Is Llama 3.1 405B Instruct or gpt-oss-120b better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 405B Instruct and gpt-oss-120b, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 3.1 405B Instruct or gpt-oss-120b?+

Only gpt-oss-120b has a directly sourced input price: $0.037 per million tokens. Only gpt-oss-120b has a directly sourced output price: $0.17 per million tokens.

Which has a larger context window, Llama 3.1 405B Instruct or gpt-oss-120b?+

Neither model has a larger sourced context window in this comparison. Llama 3.1 405B Instruct is 131K and gpt-oss-120b is 131K.

Which performs better in benchmarks, Llama 3.1 405B Instruct or gpt-oss-120b?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Llama 3.1 405B Instruct or gpt-oss-120b be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 3.1 405B Instruct is open weight; gpt-oss-120b is open weight.

Can Llama 3.1 405B Instruct and gpt-oss-120b understand images?+

Llama 3.1 405B Instruct is not documented with image input; gpt-oss-120b is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 3.1 405B Instruct or gpt-oss-120b?+

Neither has a larger sourced maximum output. Llama 3.1 405B Instruct is — and gpt-oss-120b is —.

Do Llama 3.1 405B Instruct and gpt-oss-120b support reasoning and tool use?+

Llama 3.1 405B Instruct: tool calling. gpt-oss-120b: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 3.1 405B Instruct or gpt-oss-120b?+

Llama 3.1 405B Instruct has 1 sourced provider route; gpt-oss-120b has 7, so gpt-oss-120b has broader tracked availability.

Which offers better value, Llama 3.1 405B Instruct or gpt-oss-120b?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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