Side-by-side technical and economic comparison between Mistral AI's Codestral (Current Generation) and Alibaba / Qwen's Qwen3 235B-A22B.
| Metric | Codestral (Current Generation) | Qwen3 235B-A22B | Advantage |
|---|---|---|---|
| Provider Organization | Mistral AI | Alibaba / Qwen | — |
| Standard Input / 1M | $0.30 | $0.90 | Codestral (Current Generation) (67% lower) |
| Cached Input / 1M | $0.03 | $0.18 | Codestral (Current Generation) lower |
| Output / 1M | $0.90 | $1.60 | Codestral (Current Generation) lower |
| Context Window | 256k | 128k | Codestral (Current Generation) (256k) |
| Max Generation Tokens | 8.2k | 32k | Qwen3 235B-A22B |
| Tokenizer Family | Mistral Tekken BPE (~131k vocabulary) | Qwen SentencePiece BPE (152k vocabulary) | — |
Opt for Codestral (Current Generation) when your engineering requirements prioritize Mistral AI's ecosystem, specific tokenizer efficiencies (Exact Tekken Tokenizer (±2%)), or when your expected prompt-to-completion ratios favor its $0.3/M input rate.
Opt for Qwen3 235B-A22B when looking for Alibaba / Qwen's tooling integration, specific context window depth (128k tokens), or when output generation volume favors its $1.6/M rate.