Gemma-SEA-LION-v4-27B-IT vs Llama 3.1 Nemotron 70B Instruct vs Seed 1.6
Seed 1.6 comes out ahead, 55 to 45 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
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
- 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 Llama 3.1 Nemotron 70B Instruct (45) and Gemma-SEA-LION-v4-27B-IT (39). It leads on inputs & features and context window. 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 · Llama 3.1 Nemotron 70B Instruct $0.485 per 1M tokens (3:1 blend)
- Longest contextSeed 1.6Seed 1.6 256,000 · Gemma-SEA-LION-v4-27B-IT 128,000 · Llama 3.1 Nemotron 70B Instruct 128,000 tokens
- Widest inputsSame inputsGemma-SEA-LION-v4-27B-IT: Text · Llama 3.1 Nemotron 70B Instruct: Text · Seed 1.6: Text
- Self-hostingGemma-SEA-LION-v4-27B-IT and Llama 3.1 Nemotron 70B InstructPublishes downloadable weights
| Measure | Weight | Gemma-SEA-LION-v4-27B-IT | Llama 3.1 Nemotron 70B Instruct | Seed 1.6 |
|---|---|---|---|---|
| Price | 50% | 69 | 65 | 69 |
| Inputs & features | 30% | 0 | 25 | 45 |
| Context window | 20% | 24 | 24 | 36 |
| Overall | 100% | 39/100 | 45/100 | 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 | Llama 3.1 Nemotron 70B InstructNVIDIA | Seed 1.6ByteDance Seed |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.351 | $0.478 | $0.119 (best) |
| Output | $0.555 | $0.504 (best) | $1.19 |
| Cached input | — | — | $0.024 |
| Blended (3:1) | $0.402 | $0.485 | $0.386 (best) |
| Long-context rate | Same rate | Same rate | Over 32K: $0.178 / $2.37 |
| Price source | Median of 2 providers | Median of 2 providers | Official Volcengine Ark API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 256,000 tokens (best) |
| Max output | 128,000 tokens (best) | 8,192 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 | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | nvidia/llama-3.1-nemotron-70b-instruct | doubao-seed-1-6-251015 |
| API providers | 2 | 3 (best) | 2 |
| Released | Sep 23, 2025 | Apr 15, 2025 | Oct 15, 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.
- Gemma-SEA-LION-v4-27B-IT$4.62
- Llama 3.1 Nemotron 70B Instruct$5.79
- Seed 1.6$3.56
Which should you choose?
Which is better: Gemma-SEA-LION-v4-27B-IT, Llama 3.1 Nemotron 70B Instruct or Seed 1.6?
Seed 1.6 is the better all-round choice, scoring 55/100 against Llama 3.1 Nemotron 70B Instruct (45) and Gemma-SEA-LION-v4-27B-IT (39). It leads on inputs & features and context window. 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, Llama 3.1 Nemotron 70B Instruct 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); Llama 3.1 Nemotron 70B Instruct costs $0.478 input / $0.504 output per million tokens (median across 2 API providers; free on Nvidia). 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 $0.485 for Llama 3.1 Nemotron 70B Instruct (1.3× 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, Llama 3.1 Nemotron 70B Instruct 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, Llama 3.1 Nemotron 70B Instruct 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?
Seed 1.6 has the largest context window at 256,000 tokens, against 128,000 for Gemma-SEA-LION-v4-27B-IT and 128,000 for Llama 3.1 Nemotron 70B Instruct. Maximum output per response: Gemma-SEA-LION-v4-27B-IT up to 128,000, Llama 3.1 Nemotron 70B Instruct up to 8,192, Seed 1.6 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Gemma-SEA-LION-v4-27B-IT accepts text; Llama 3.1 Nemotron 70B Instruct 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 Llama 3.1 Nemotron 70B Instruct publishes its weights and can be self-hosted; Seed 1.6 is proprietary.
Which is newer?
Seed 1.6 is the newest, released Oct 15, 2025. Gemma-SEA-LION-v4-27B-IT came out Sep 23, 2025; Llama 3.1 Nemotron 70B Instruct came out Apr 15, 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.
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