Compare
gpt-oss-120b vs Llama 3 3 70b Versatile
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Model
Llama 3 3 70b Versatile
Context window
128K
128,000 tokens · ~96K words
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
gpt-oss-120b has about 1× the context window of the other in this pair.
gpt-oss-120b has 2% more context capacity (131K vs 128K tokens). gpt-oss-120b is 91% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use gpt-oss-120b. Its 131K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use gpt-oss-120b. Input tokens are 91% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use gpt-oss-120b. Its 131K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | gpt-oss-120b | Llama 3 3 70b Versatile |
|---|---|---|
| Context window | 131,072 tokens (131K) | 128,000 tokens (128K) |
| Max output tokens | 131,072 tokens (131K) | 32,768 tokens (32K) |
| Speed tier | Balanced | Deep |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Aug 2025 | N/A |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | gpt-oss-120b in | gpt-oss-120b out | Llama 3 3 70b Versatile in | Llama 3 3 70b Versatile out |
|---|---|---|---|---|
| Azure | $0.150/M | $0.600/M | — | — |
| Baseten | $0.100/M | $0.500/M | — | — |
| Cerebras | $0.350/M | $0.750/M | — | — |
| Deepinfra | $0.050/M | $0.450/M | — | — |
| Fireworks | $0.150/M | $0.600/M | — | — |
| Groq | $0.150/M | $0.600/M | $0.590/M | $0.790/M |
| Ibm Watsonx | $0.150/M | $0.600/M | — | — |
| Novita | $0.050/M | $0.250/M | — | — |
| Openrouter | $0.180/M | $0.800/M | — | — |
| Ovhcloud | $0.080/M | $0.400/M | — | — |
| Replicate | $0.180/M | $0.720/M | — | — |
| Sambanova | $3.00/M | $4.50/M | — | — |
| Together Ai | $0.150/M | $0.600/M | — | — |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model