Gemma-SEA-LION-v4-27B-IT vs Qwen3 Coder Next vs Seed 1.6
Seed 1.6 comes out ahead, 55 to 51 and 39 on our weighted score, and it is the cheaper option too.
AI Singapore
Gemma-SEA-LION-v4-27B-IT
39/100- ECI—
- Price$0.351 / $0.555
- Context128K
Alibaba (Qwen)Qwen3 Coder Next
51/100- ECI—
- Price$0.20 / $1.20
- Context262K
- Our pick
ByteDance SeedSeed 1.6
55/100- ECI—
- Price$0.119 / $1.19
- Context256K
Seed 1.6 is our pick
Seed 1.6 is the better all-round choice, scoring 55/100 against Qwen3 Coder Next (51) and Gemma-SEA-LION-v4-27B-IT (39). 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 priceSeed 1.6Seed 1.6 $0.386 · Gemma-SEA-LION-v4-27B-IT $0.402 · Qwen3 Coder Next $0.45 per 1M tokens (3:1 blend)
- Longest contextQwen3 Coder NextQwen3 Coder Next 262,144 · Seed 1.6 256,000 · Gemma-SEA-LION-v4-27B-IT 128,000 tokens
- Widest inputsSame inputsGemma-SEA-LION-v4-27B-IT: Text · Qwen3 Coder Next: Text · Seed 1.6: Text
- Self-hostingGemma-SEA-LION-v4-27B-IT and Qwen3 Coder NextPublishes downloadable weights
| Measure | Weight | Gemma-SEA-LION-v4-27B-IT | Qwen3 Coder Next | Seed 1.6 |
|---|---|---|---|---|
| Price | 50% | 69 | 66 | 69 |
| Inputs & features | 30% | 0 | 35 | 45 |
| Context window | 20% | 24 | 37 | 36 |
| Overall | 100% | 39/100 | 51/100 | 55/100 |
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 | Gemma-SEA-LION-v4-27B-ITAI Singapore | Qwen3 Coder NextAlibaba (Qwen) | Seed 1.6ByteDance Seed |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.351 | $0.20 | $0.119 (best) |
| Output | $0.555 (best) | $1.20 | $1.19 |
| Cached input | — | — | $0.024 |
| Blended (3:1) | $0.402 | $0.45 | $0.386 (best) |
| Long-context rate | Same rate | Same rate | Over 32K: $0.178 / $2.37 |
| Price source | Median of 2 providers | Median of 11 providers | Official Volcengine Ark API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 256,000 tokens |
| Max output | 128,000 tokens (best) | 65,536 tokens | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yesminimal · low · medium · high |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | — | doubao-seed-1-6-251015 |
| API providers | 2 | 11 (best) | 2 |
| Released | Sep 23, 2025 | Feb 3, 2026 | Oct 15, 2025 |
| Knowledge cutoff | — | Sep 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemma-SEA-LION-v4-27B-IT$4.62
- Qwen3 Coder Next$4.40
- Seed 1.6$3.56
Which should you choose?
Which is better: Gemma-SEA-LION-v4-27B-IT, Qwen3 Coder Next or Seed 1.6?
Seed 1.6 is the better all-round choice, scoring 55/100 against Qwen3 Coder Next (51) and Gemma-SEA-LION-v4-27B-IT (39). 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, Gemma-SEA-LION-v4-27B-IT, Qwen3 Coder Next or Seed 1.6?
Seed 1.6 is cheaper at $0.119 input / $1.19 output per million tokens (official Volcengine Ark API price). Gemma-SEA-LION-v4-27B-IT costs $0.351 input / $0.555 output per million tokens (median across 2 API providers); Qwen3 Coder Next costs $0.20 input / $1.20 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.386 per million tokens for Seed 1.6 versus $0.402 for Gemma-SEA-LION-v4-27B-IT (1× as much) and $0.45 for Qwen3 Coder Next (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemma-SEA-LION-v4-27B-IT has not been scored yet, Qwen3 Coder Next has not been scored yet and Seed 1.6 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma-SEA-LION-v4-27B-IT, Qwen3 Coder Next and Seed 1.6 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?
Qwen3 Coder Next has the largest context window at 262,144 tokens, against 256,000 for Seed 1.6 and 128,000 for Gemma-SEA-LION-v4-27B-IT. Maximum output per response: Gemma-SEA-LION-v4-27B-IT up to 128,000, Qwen3 Coder Next up to 65,536, Seed 1.6 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Gemma-SEA-LION-v4-27B-IT accepts text; Qwen3 Coder Next accepts text; Seed 1.6 accepts text. They handle the same number of input types.
Are any of these open source?
Gemma-SEA-LION-v4-27B-IT and Qwen3 Coder Next publishes its weights and can be self-hosted; Seed 1.6 is proprietary.
Which is newer?
Qwen3 Coder Next is the newest, released Feb 3, 2026. Seed 1.6 came out Oct 15, 2025; Gemma-SEA-LION-v4-27B-IT came out Sep 23, 2025. Knowledge cutoff: Qwen3 Coder Next Sep 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 Oct 4, 2026.
