Gemma-SEA-LION-v4-27B-IT vs GLM-4.5-Air vs Llama 3.3 Nemotron Super 49B v1.5
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1.5 50, GLM-4.5-Air 49, Gemma-SEA-LION-v4-27B-IT 39). The right pick depends on what you value most.
AI Singapore
Gemma-SEA-LION-v4-27B-IT
39/100- ECI—
- Price$0.351 / $0.555
- Context128K
Z.ai (Zhipu)GLM-4.5-Air
49/100- ECI—
- Price$0.20 / $1.10
- Context131K
NVIDIALlama 3.3 Nemotron Super 49B v1.5
50/100- ECI—
- Price$0.40 / $0.40
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (Llama 3.3 Nemotron Super 49B v1.5 50/100, GLM-4.5-Air 49/100, Gemma-SEA-LION-v4-27B-IT 39/100), so choose by what matters most for your work: Llama 3.3 Nemotron Super 49B v1.5 on price. 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 priceLlama 3.3 Nemotron Super 49B v1.5Llama 3.3 Nemotron Super 49B v1.5 $0.40 · Gemma-SEA-LION-v4-27B-IT $0.402 · GLM-4.5-Air $0.425 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-Air and Llama 3.3 Nemotron Super 49B v1.5GLM-4.5-Air 131,072 · Llama 3.3 Nemotron Super 49B v1.5 131,072 · Gemma-SEA-LION-v4-27B-IT 128,000 tokens
- Widest inputsSame inputsGemma-SEA-LION-v4-27B-IT: Text · GLM-4.5-Air: Text · Llama 3.3 Nemotron Super 49B v1.5: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Gemma-SEA-LION-v4-27B-IT | GLM-4.5-Air | Llama 3.3 Nemotron Super 49B v1.5 |
|---|---|---|---|---|
| Price | 50% | 69 | 68 | 69 |
| Inputs & features | 30% | 0 | 35 | 35 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 39/100 | 49/100 | 50/100 |
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 | GLM-4.5-AirZ.ai (Zhipu) | Llama 3.3 Nemotron Super 49B v1.5NVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.351 | $0.20 (best) | $0.40 |
| Output | $0.555 | $1.10 | $0.40 (best) |
| Cached input | — | $0.03 | — |
| Blended (3:1) | $0.402 | $0.425 | $0.40 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Z.AI API | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 128,000 tokens | 98,304 tokens | 131,072 tokens (best) |
| 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 | Yes | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | glm-4.5-air | nvidia/llama-3.3-nemotron-super-49b-v1.5 |
| API providers | 2 | 13 (best) | 2 |
| Released | Sep 23, 2025 | Jul 28, 2025 | Jul 25, 2025 |
| Knowledge cutoff | — | Apr 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
- GLM-4.5-Air$4.20
- Llama 3.3 Nemotron Super 49B v1.5$4.80
Which should you choose?
Which is better: Gemma-SEA-LION-v4-27B-IT, GLM-4.5-Air or Llama 3.3 Nemotron Super 49B v1.5?
It is close. Our weighted score puts them within a point (Llama 3.3 Nemotron Super 49B v1.5 50/100, GLM-4.5-Air 49/100, Gemma-SEA-LION-v4-27B-IT 39/100), so choose by what matters most for your work: Llama 3.3 Nemotron Super 49B v1.5 on price. 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, GLM-4.5-Air or Llama 3.3 Nemotron Super 49B v1.5?
Llama 3.3 Nemotron Super 49B v1.5 is cheaper at $0.40 input / $0.40 output per million tokens (median across 1 API provider; free on Nvidia). Gemma-SEA-LION-v4-27B-IT costs $0.351 input / $0.555 output per million tokens (median across 2 API providers); GLM-4.5-Air costs $0.20 input / $1.10 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.40 per million tokens for Llama 3.3 Nemotron Super 49B v1.5 versus $0.402 for Gemma-SEA-LION-v4-27B-IT (1× as much) and $0.425 for GLM-4.5-Air (1.1× 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, GLM-4.5-Air has not been scored yet and Llama 3.3 Nemotron Super 49B v1.5 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, GLM-4.5-Air and Llama 3.3 Nemotron Super 49B v1.5 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?
GLM-4.5-Air and Llama 3.3 Nemotron Super 49B v1.5 have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Gemma-SEA-LION-v4-27B-IT. Maximum output per response: Gemma-SEA-LION-v4-27B-IT up to 128,000, GLM-4.5-Air up to 98,304, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Gemma-SEA-LION-v4-27B-IT accepts text; GLM-4.5-Air accepts text; Llama 3.3 Nemotron Super 49B v1.5 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Gemma-SEA-LION-v4-27B-IT is the newest, released Sep 23, 2025. GLM-4.5-Air came out Jul 28, 2025; Llama 3.3 Nemotron Super 49B v1.5 came out Jul 25, 2025. Knowledge cutoff: GLM-4.5-Air Apr 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.
