Mistral Large 3 vs babbage-002
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Mistral Large 3 | babbage-002 |
|---|---|---|
| Provider | Mistral | OpenAI |
| Input Price (1M) | $0.50 | $0.40 |
| Output Price (1M) | $1.50 | $0.40 |
| Context Window | 262,000 | 16,384 |
Verdict
Mistral Large 3 costs $0.50 per 1M input tokens and $1.50 per 1M output tokens. babbage-002 costs $0.40 per 1M input tokens and $0.40 per 1M output tokens. babbage-002 is 20% cheaper on input tokens than Mistral Large 3. For output tokens, babbage-002 is the more affordable option at $0.40/1M vs $1.50.
On context window, Mistral Large 3 supports 262,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Mistral Large 3
- ✓ You need a larger context window (262,000 tokens)
- ✓ You are already integrated with Mistral
When to choose babbage-002
- ✓ You need the lowest input token cost ($ 0.40/1M)
- ✓ Your workload is output-heavy — babbage-002 generates text cheaper
- ✓ You are already integrated with OpenAI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Mistral Large 3 cheaper than babbage-002? ▼
babbage-002 is cheaper on input tokens at $0.40/1M vs $0.50/1M for Mistral Large 3 — a 20% saving.
What is the context window of Mistral Large 3 vs babbage-002? ▼
Mistral Large 3 has a 262,000-token context window. babbage-002 has a 16,384-token context window. Mistral Large 3 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Mistral Large 3 or babbage-002? ▼
The best choice depends on your use case. For cost efficiency on input tokens, babbage-002 is the cheaper option. For maximum context length, Mistral Large 3 supports 262,000 tokens. Use the comparison table above to find the right fit for your workload.