Llama 3.3 Nemotron Super 49B v1.5 vs Qwen3 Omni 30B A3B Thinking vs GLM-4.5-Air
Qwen3 Omni 30B A3B Thinking comes out ahead, 59 to 48 and 48 on our weighted score, though Llama 3.3 Nemotron Super 49B v1.5 is 7% cheaper per token.
NVIDIALlama 3.3 Nemotron Super 49B v1.5
48/100- ECI—
- Price$0.40 / $0.40
- Context131K
- Our pick
Alibaba (Qwen)Qwen3 Omni 30B A3B Thinking
59/100- ECI—
- Price$0.25 / $0.97
- Context66K
Z.ai (Zhipu)GLM-4.5-Air
48/100- ECI—
- Price$0.20 / $1.10
- Context131K
Qwen3 Omni 30B A3B Thinking is our pick
Qwen3 Omni 30B A3B Thinking is the better all-round choice, scoring 59/100 against Llama 3.3 Nemotron Super 49B v1.5 (48) and GLM-4.5-Air (48). 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 priceLlama 3.3 Nemotron Super 49B v1.5Llama 3.3 Nemotron Super 49B v1.5 $0.40 · GLM-4.5-Air $0.425 · Qwen3 Omni 30B A3B Thinking $0.43 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-AirLlama 3.3 Nemotron Super 49B v1.5 131,072 · GLM-4.5-Air 131,072 · Qwen3 Omni 30B A3B Thinking 65,536 tokens
- Widest inputsQwen3 Omni 30B A3B ThinkingLlama 3.3 Nemotron Super 49B v1.5: Text · Qwen3 Omni 30B A3B Thinking: Text, Images, Audio, Video · GLM-4.5-Air: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama 3.3 Nemotron Super 49B v1.5 | Qwen3 Omni 30B A3B Thinking | GLM-4.5-Air |
|---|---|---|---|---|
| Price | 50% | 66 | 64 | 65 |
| Inputs & features | 30% | 35 | 80 | 35 |
| Context window | 20% | 25 | 12 | 25 |
| Overall | 100% | 48/100 | 59/100 | 48/100 |
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 | Llama 3.3 Nemotron Super 49B v1.5NVIDIA | Qwen3 Omni 30B A3B ThinkingAlibaba (Qwen) | GLM-4.5-AirZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.40 | $0.25 | $0.20 (best) |
| Output | $0.40 (best) | $0.97 | $1.10 |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $0.40 (best) | $0.43 | $0.425 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 1 providers | Official Z.AI API |
| Limits | |||
| Context window | 131,072 tokens (best) | 65,536 tokens | 131,072 tokens (best) |
| Max output | 131,072 tokens (best) | 16,384 tokens | 98,304 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenApache-2.0 | Open |
| API model ID | — | — | glm-4.5-air |
| API providers | 1 | 1 | 19 (best) |
| Released | Jul 25, 2025 | Sep 22, 2025 | Jul 28, 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.
- Llama 3.3 Nemotron Super 49B v1.5$4.80
- Qwen3 Omni 30B A3B Thinking$4.44
- GLM-4.5-Air$4.20
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1.5, Qwen3 Omni 30B A3B Thinking or GLM-4.5-Air?
Qwen3 Omni 30B A3B Thinking is the better all-round choice, scoring 59/100 against Llama 3.3 Nemotron Super 49B v1.5 (48) and GLM-4.5-Air (48). 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, Llama 3.3 Nemotron Super 49B v1.5, Qwen3 Omni 30B A3B Thinking or GLM-4.5-Air?
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); Qwen3 Omni 30B A3B Thinking costs $0.25 input / $0.97 output per million tokens (median across 1 API provider). 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) and $0.43 for Qwen3 Omni 30B A3B Thinking (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama 3.3 Nemotron Super 49B v1.5 has not been scored yet, Qwen3 Omni 30B A3B Thinking has not been scored yet and GLM-4.5-Air has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 3.3 Nemotron Super 49B v1.5, Qwen3 Omni 30B A3B Thinking and GLM-4.5-Air 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?
Llama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-Air have the largest context windows (131,072 and 131,072 tokens), against 65,536 for Qwen3 Omni 30B A3B Thinking. Maximum output per response: Llama 3.3 Nemotron Super 49B v1.5 up to 131,072, Qwen3 Omni 30B A3B Thinking up to 16,384, GLM-4.5-Air up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Llama 3.3 Nemotron Super 49B v1.5 accepts text; Qwen3 Omni 30B A3B Thinking accepts text, images, audio and video; GLM-4.5-Air accepts text. Qwen3 Omni 30B A3B Thinking handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
Qwen3 Omni 30B A3B Thinking is the newest, released Sep 22, 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 Nov 2, 2026.
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