GLM-4.5-Air vs Llama 3.3 Nemotron Super 49B v1.5 vs Seed-OSS-36B-Instruct
Seed-OSS-36B-Instruct comes out ahead, 40 to 31 and 31 on our weighted score.
Z.ai (Zhipu)GLM-4.5-Air
31/100- ECI—
- Price$0.20 / $1.10
- Context131K
NVIDIALlama 3.3 Nemotron Super 49B v1.5
31/100- ECI—
- Price$0.40 / $0.40
- Context131K
- Our pick
ByteDance SeedSeed-OSS-36B-Instruct
40/100- ECI—
- Price—
- Context524K
Seed-OSS-36B-Instruct is our pick
Seed-OSS-36B-Instruct is the better all-round choice, scoring 40/100 against GLM-4.5-Air (31) and Llama 3.3 Nemotron Super 49B v1.5 (31). It leads 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 priceLlama 3.3 Nemotron Super 49B v1.5Llama 3.3 Nemotron Super 49B v1.5 $0.40 · GLM-4.5-Air $0.425 per 1M tokens (3:1 blend) · Seed-OSS-36B-Instruct unpriced
- Longest contextSeed-OSS-36B-InstructSeed-OSS-36B-Instruct 524,288 · GLM-4.5-Air 131,072 · Llama 3.3 Nemotron Super 49B v1.5 131,072 tokens
- Widest inputsSame inputsGLM-4.5-Air: Text · Llama 3.3 Nemotron Super 49B v1.5: Text · Seed-OSS-36B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5-Air | Llama 3.3 Nemotron Super 49B v1.5 | Seed-OSS-36B-Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 35 | 35 | 35 |
| Context window | 40% | 25 | 25 | 49 |
| Overall | 100% | 31/100 | 31/100 | 40/100 |
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 | GLM-4.5-AirZ.ai (Zhipu) | Llama 3.3 Nemotron Super 49B v1.5NVIDIA | Seed-OSS-36B-InstructByteDance Seed |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | — | 71.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 67.5% |
| Price per million tokens | |||
| Input | $0.20 (best) | $0.40 | — |
| Output | $1.10 | $0.40 (best) | — |
| Cached input | $0.03 | — | — |
| Blended (3:1) | $0.425 | $0.40 (best) | — |
| Long-context rate | Same rate | Same rate | — |
| Price source | Official Z.AI API | Median of 1 providers | — |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 524,288 tokens (best) |
| Max output | 98,304 tokens | 131,072 tokens | 524,288 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 | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | glm-4.5-air | — | — |
| API providers | 19 (best) | 1 | — |
| Released | Jul 28, 2025 | Jul 25, 2025 | Aug 20, 2025 |
| Knowledge cutoff | Apr 2025 | — | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.5-Air$4.20
- Llama 3.3 Nemotron Super 49B v1.5$4.80
- Seed-OSS-36B-Instruct—
Which should you choose?
Which is better: GLM-4.5-Air, Llama 3.3 Nemotron Super 49B v1.5 or Seed-OSS-36B-Instruct?
Seed-OSS-36B-Instruct is the better all-round choice, scoring 40/100 against GLM-4.5-Air (31) and Llama 3.3 Nemotron Super 49B v1.5 (31). It leads 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, GLM-4.5-Air, Llama 3.3 Nemotron Super 49B v1.5 or Seed-OSS-36B-Instruct?
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). 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 $0.425 for GLM-4.5-Air (1.1× 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. GLM-4.5-Air has not been scored yet, Llama 3.3 Nemotron Super 49B v1.5 has not been scored yet and Seed-OSS-36B-Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Air, Llama 3.3 Nemotron Super 49B v1.5 and Seed-OSS-36B-Instruct 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 131,072 for GLM-4.5-Air and 131,072 for Llama 3.3 Nemotron Super 49B v1.5. Maximum output per response: GLM-4.5-Air up to 98,304, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072, Seed-OSS-36B-Instruct up to 524,288 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Air accepts text; Llama 3.3 Nemotron Super 49B v1.5 accepts text; Seed-OSS-36B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Seed-OSS-36B-Instruct is the newest, released Aug 20, 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, Seed-OSS-36B-Instruct Jul 2024.
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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