Llama 3.3 Nemotron Super 49B v1.5 vs Codestral 25.08 vs GLM-4.5-Air
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1.5 48, GLM-4.5-Air 48, Codestral 25.08 47). The right pick depends on what you value most.
NVIDIALlama 3.3 Nemotron Super 49B v1.5
48/100- ECI—
- Price$0.40 / $0.40
- Context131K
Mistral AICodestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
Z.ai (Zhipu)GLM-4.5-Air
48/100- ECI—
- Price$0.20 / $1.10
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (Llama 3.3 Nemotron Super 49B v1.5 48/100, GLM-4.5-Air 48/100, Codestral 25.08 47/100), 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 · GLM-4.5-Air $0.425 · Codestral 25.08 $0.45 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 · Codestral 25.08 128,000 tokens
- Widest inputsSame inputsLlama 3.3 Nemotron Super 49B v1.5: Text · Codestral 25.08: Text · GLM-4.5-Air: Text
- Self-hostingLlama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-AirPublishes downloadable weights
| Measure | Weight | Llama 3.3 Nemotron Super 49B v1.5 | Codestral 25.08 | GLM-4.5-Air |
|---|---|---|---|---|
| Price | 50% | 66 | 64 | 65 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 25 | 24 | 25 |
| Overall | 100% | 48/100 | 47/100 | 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 | Codestral 25.08Mistral AI | GLM-4.5-AirZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.40 | $0.30 | $0.20 (best) |
| Output | $0.40 (best) | $0.90 | $1.10 |
| Cached input | — | $0.03 | $0.03 |
| Blended (3:1) | $0.40 (best) | $0.45 | $0.425 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Mistral API | Official Z.AI API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 131,072 tokens (best) | 8,192 tokens | 98,304 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 | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | codestral-2508 | glm-4.5-air |
| API providers | 1 | 11 | 19 (best) |
| Released | Jul 25, 2025 | Jul 30, 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
- Codestral 25.08$4.80
- GLM-4.5-Air$4.20
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1.5, Codestral 25.08 or GLM-4.5-Air?
It is close. Our weighted score puts them within a point (Llama 3.3 Nemotron Super 49B v1.5 48/100, GLM-4.5-Air 48/100, Codestral 25.08 47/100), 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, Llama 3.3 Nemotron Super 49B v1.5, Codestral 25.08 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); Codestral 25.08 costs $0.30 input / $0.90 output per million tokens (official Mistral 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) and $0.45 for Codestral 25.08 (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, Codestral 25.08 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, Codestral 25.08 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 128,000 for Codestral 25.08. Maximum output per response: Llama 3.3 Nemotron Super 49B v1.5 up to 131,072, Codestral 25.08 up to 8,192, 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; Codestral 25.08 accepts text; GLM-4.5-Air accepts text. They handle the same number of input types.
Are any of these open source?
Llama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-Air publishes its weights and can be self-hosted; Codestral 25.08 is proprietary.
Which is newer?
Codestral 25.08 is the newest, released Jul 30, 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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