Codestral 25.08 vs Gemma-SEA-LION-v4-27B-IT vs Qwen3 Omni 30B A3B Thinking
Qwen3 Omni 30B A3B Thinking comes out ahead, 59 to 47 and 45 on our weighted score, though Gemma-SEA-LION-v4-27B-IT is 7% cheaper per token.
Mistral AICodestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
AI Singapore
Gemma-SEA-LION-v4-27B-IT
45/100- ECI—
- Price$0.351 / $0.555
- Context128K
- Our pick
Alibaba (Qwen)Qwen3 Omni 30B A3B Thinking
59/100- ECI—
- Price$0.25 / $0.97
- Context66K
Qwen3 Omni 30B A3B Thinking is our pick
Qwen3 Omni 30B A3B Thinking is the better all-round choice, scoring 59/100 against Codestral 25.08 (47) and Gemma-SEA-LION-v4-27B-IT (45). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. 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 priceGemma-SEA-LION-v4-27B-ITGemma-SEA-LION-v4-27B-IT $0.402 · Qwen3 Omni 30B A3B Thinking $0.43 · Codestral 25.08 $0.45 per 1M tokens (3:1 blend)
- Longest contextCodestral 25.08 and Gemma-SEA-LION-v4-27B-ITCodestral 25.08 128,000 · Gemma-SEA-LION-v4-27B-IT 128,000 · Qwen3 Omni 30B A3B Thinking 65,536 tokens
- Widest inputsQwen3 Omni 30B A3B ThinkingCodestral 25.08: Text · Gemma-SEA-LION-v4-27B-IT: Text, Images · Qwen3 Omni 30B A3B Thinking: Text, Images, Audio, Video
- Self-hostingGemma-SEA-LION-v4-27B-IT and Qwen3 Omni 30B A3B ThinkingPublishes downloadable weights (Apache-2.0)
| Measure | Weight | Codestral 25.08 | Gemma-SEA-LION-v4-27B-IT | Qwen3 Omni 30B A3B Thinking |
|---|---|---|---|---|
| Price | 50% | 64 | 66 | 64 |
| Inputs & features | 30% | 35 | 25 | 80 |
| Context window | 20% | 24 | 24 | 12 |
| Overall | 100% | 47/100 | 45/100 | 59/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Codestral 25.08Mistral AI | Gemma-SEA-LION-v4-27B-ITAI Singapore | Qwen3 Omni 30B A3B ThinkingAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.351 | $0.25 (best) |
| Output | $0.90 | $0.555 (best) | $0.97 |
| Cached input | $0.03 | — | — |
| Blended (3:1) | $0.45 | $0.402 (best) | $0.43 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Median of 3 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens (best) | 128,000 tokens (best) | 65,536 tokens |
| Max output | 8,192 tokens | 128,000 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | No | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenApache-2.0 |
| API model ID | codestral-2508 | — | — |
| API providers | 11 (best) | 3 | 1 |
| Released | Jul 30, 2025 | Sep 23, 2025 | Sep 22, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Codestral 25.08$4.80
- Gemma-SEA-LION-v4-27B-IT$4.62
- Qwen3 Omni 30B A3B Thinking$4.44
Which should you choose?
Which is better: Codestral 25.08, Gemma-SEA-LION-v4-27B-IT or Qwen3 Omni 30B A3B Thinking?
Qwen3 Omni 30B A3B Thinking is the better all-round choice, scoring 59/100 against Codestral 25.08 (47) and Gemma-SEA-LION-v4-27B-IT (45). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Codestral 25.08, Gemma-SEA-LION-v4-27B-IT or Qwen3 Omni 30B A3B Thinking?
Gemma-SEA-LION-v4-27B-IT is cheaper at $0.351 input / $0.555 output per million tokens (median across 3 API providers). Qwen3 Omni 30B A3B Thinking costs $0.25 input / $0.97 output per million tokens (median across 1 API provider); Codestral 25.08 costs $0.30 input / $0.90 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.402 per million tokens for Gemma-SEA-LION-v4-27B-IT versus $0.43 for Qwen3 Omni 30B A3B Thinking (1.1× as much) and $0.45 for Codestral 25.08 (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Codestral 25.08 has not been scored yet, Gemma-SEA-LION-v4-27B-IT has not been scored yet and Qwen3 Omni 30B A3B Thinking has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Codestral 25.08, Gemma-SEA-LION-v4-27B-IT and Qwen3 Omni 30B A3B Thinking yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Gemma-SEA-LION-v4-27B-IT does not support tool calling, which most coding agents need.
Which has the bigger context window?
Codestral 25.08 and Gemma-SEA-LION-v4-27B-IT have the largest context windows (128,000 and 128,000 tokens), against 65,536 for Qwen3 Omni 30B A3B Thinking. Maximum output per response: Codestral 25.08 up to 8,192, Gemma-SEA-LION-v4-27B-IT up to 128,000, Qwen3 Omni 30B A3B Thinking up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Codestral 25.08 accepts text; Gemma-SEA-LION-v4-27B-IT accepts text and images; Qwen3 Omni 30B A3B Thinking accepts text, images, audio and video. Qwen3 Omni 30B A3B Thinking handles the widest range of inputs.
Are any of these open source?
Gemma-SEA-LION-v4-27B-IT and Qwen3 Omni 30B A3B Thinking publishes its weights (Apache-2.0) and can be self-hosted; Codestral 25.08 is proprietary.
Which is newer?
Gemma-SEA-LION-v4-27B-IT is the newest, released Sep 23, 2025. Qwen3 Omni 30B A3B Thinking came out Sep 22, 2025; Codestral 25.08 came out Jul 30, 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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