Seed-OSS-36B-Instruct vs Codestral 25.08 vs Claude Haiku 4.5
Claude Haiku 4.5 comes out ahead, 61 to 40 and 31 on our weighted score, though Codestral 25.08 is 4.4× cheaper per token.
ByteDance SeedSeed-OSS-36B-Instruct
40/100- ECI—
- Price—
- Context524K
Mistral AICodestral 25.08
31/100- ECI—
- Price$0.30 / $0.90
- Context128K
- Our pick
AnthropicClaude Haiku 4.5
61/100- ECI142.2
- Price$1.00 / $5.00
- Context200K
Claude Haiku 4.5 is our pick
Claude Haiku 4.5 is the better all-round choice, scoring 61/100 against Seed-OSS-36B-Instruct (40) and Codestral 25.08 (31). It leads on inputs & features. Seed-OSS-36B-Instruct wins on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceCodestral 25.08Codestral 25.08 $0.45 · Claude Haiku 4.5 $2.00 per 1M tokens (3:1 blend) · Seed-OSS-36B-Instruct unpriced
- Longest contextSeed-OSS-36B-InstructSeed-OSS-36B-Instruct 524,288 · Claude Haiku 4.5 200,000 · Codestral 25.08 128,000 tokens
- Widest inputsClaude Haiku 4.5Seed-OSS-36B-Instruct: Text · Codestral 25.08: Text · Claude Haiku 4.5: Text, Images, PDFs
- Self-hostingSeed-OSS-36B-InstructPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Seed-OSS-36B-Instruct | Codestral 25.08 | Claude Haiku 4.5 |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 35 | 80 |
| Context window | 40% | 49 | 24 | 32 |
| Overall | 100% | 40/100 | 31/100 | 61/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Seed-OSS-36B-InstructByteDance Seed | Codestral 25.08Mistral AI | Claude Haiku 4.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 142.2 |
| ECI rank | — | — | #102 of 165 |
| GPQA DiamondGraduate-level science questions | 71.5% (best) | — | 71.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 67.5% (best) | — | 66.7% |
| SimpleQA VerifiedShort factual questions | — | — | 13.2% |
| Price per million tokens | |||
| Input | — | $0.30 (best) | $1.00 |
| Output | — | $0.90 (best) | $5.00 |
| Cached input | — | $0.03 (best) | $0.10 |
| Blended (3:1) | — | $0.45 (best) | $2.00 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Mistral API | Official Anthropic API |
| Limits | |||
| Context window | 524,288 tokens (best) | 128,000 tokens | 200,000 tokens |
| Max output | 524,288 tokens (best) | 8,192 tokens | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenApache 2.0 | Proprietary | Proprietary |
| API model ID | — | codestral-2508 | claude-haiku-4-5 |
| API providers | — | 11 | 48 (best) |
| Released | Aug 20, 2025 | Jul 30, 2025 | Oct 15, 2025 |
| Knowledge cutoff | Jul 2024 | — | Feb 28, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Seed-OSS-36B-Instruct—
- Codestral 25.08$4.80
- Claude Haiku 4.5$20.00
Which should you choose?
Which is better: Seed-OSS-36B-Instruct, Codestral 25.08 or Claude Haiku 4.5?
Claude Haiku 4.5 is the better all-round choice, scoring 61/100 against Seed-OSS-36B-Instruct (40) and Codestral 25.08 (31). It leads on inputs & features. Seed-OSS-36B-Instruct wins on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Seed-OSS-36B-Instruct, Codestral 25.08 or Claude Haiku 4.5?
Codestral 25.08 is cheaper at $0.30 input / $0.90 output per million tokens (official Mistral API price). Claude Haiku 4.5 costs $1.00 input / $5.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $0.45 per million tokens for Codestral 25.08 versus $2.00 for Claude Haiku 4.5 (4.4× as much). Seed-OSS-36B-Instruct has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Seed-OSS-36B-Instruct has not been scored yet, Codestral 25.08 has not been scored yet and Claude Haiku 4.5 has an ECI of 142.2.
Which is better for coding?
There are no published SWE-bench Verified results for Seed-OSS-36B-Instruct, Codestral 25.08 and Claude Haiku 4.5 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Seed-OSS-36B-Instruct has the largest context window at 524,288 tokens, against 200,000 for Claude Haiku 4.5 and 128,000 for Codestral 25.08. Maximum output per response: Seed-OSS-36B-Instruct up to 524,288, Codestral 25.08 up to 8,192, Claude Haiku 4.5 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Seed-OSS-36B-Instruct accepts text; Codestral 25.08 accepts text; Claude Haiku 4.5 accepts text, images and PDFs. Claude Haiku 4.5 handles the widest range of inputs.
Are any of these open source?
Seed-OSS-36B-Instruct publishes its weights (Apache 2.0) and can be self-hosted; Codestral 25.08 and Claude Haiku 4.5 is proprietary.
Which is newer?
Claude Haiku 4.5 is the newest, released Oct 15, 2025. Seed-OSS-36B-Instruct came out Aug 20, 2025; Codestral 25.08 came out Jul 30, 2025. Knowledge cutoff: Seed-OSS-36B-Instruct Jul 2024, Claude Haiku 4.5 Feb 28, 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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