GLM-4.5-Air vs Codestral 25.08 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 48, GLM-4.5-Air 48, Codestral 25.08 47). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5-Air
48/100- ECI—
- Price$0.20 / $1.10
- Context131K
Mistral AICodestral 25.08
47/100- ECI—
- Price$0.30 / $0.90
- Context128K
NVIDIALlama 3.3 Nemotron Super 49B v1.5
48/100- 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 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 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 · Codestral 25.08 128,000 tokens
- Widest inputsSame inputsGLM-4.5-Air: Text · Codestral 25.08: Text · Llama 3.3 Nemotron Super 49B v1.5: Text
- Self-hostingGLM-4.5-Air and Llama 3.3 Nemotron Super 49B v1.5Publishes downloadable weights
| Measure | Weight | GLM-4.5-Air | Codestral 25.08 | Llama 3.3 Nemotron Super 49B v1.5 |
|---|---|---|---|---|
| Price | 50% | 65 | 64 | 66 |
| 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 | GLM-4.5-AirZ.ai (Zhipu) | Codestral 25.08Mistral AI | Llama 3.3 Nemotron Super 49B v1.5NVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.20 (best) | $0.30 | $0.40 |
| Output | $1.10 | $0.90 | $0.40 (best) |
| Cached input | $0.03 | $0.03 | — |
| Blended (3:1) | $0.425 | $0.45 | $0.40 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Mistral API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 98,304 tokens | 8,192 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 | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.5-air | codestral-2508 | — |
| API providers | 19 (best) | 11 | 1 |
| Released | Jul 28, 2025 | Jul 30, 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.
- GLM-4.5-Air$4.20
- Codestral 25.08$4.80
- Llama 3.3 Nemotron Super 49B v1.5$4.80
Which should you choose?
Which is better: GLM-4.5-Air, Codestral 25.08 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 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, GLM-4.5-Air, Codestral 25.08 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). 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. GLM-4.5-Air has not been scored yet, Codestral 25.08 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 GLM-4.5-Air, Codestral 25.08 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. All three support tool calling for agent workflows.
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 Codestral 25.08. Maximum output per response: GLM-4.5-Air up to 98,304, Codestral 25.08 up to 8,192, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Air accepts text; Codestral 25.08 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?
GLM-4.5-Air and Llama 3.3 Nemotron Super 49B v1.5 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.
Related comparisons
- GLM-4.5-Air vs Qwen3 Omni 30B A3B ThinkingAlibaba (Qwen)
- GLM-4.5-Air vs ERNIE 4.5 300B A47BBaidu
- Codestral 25.08 vs ERNIE 4.5 300B A47BBaidu
- Codestral 25.08 vs Qwen3 Omni 30B A3B ThinkingAlibaba (Qwen)
- Llama 3.3 Nemotron Super 49B v1.5 vs Gemma-SEA-LION-v4-27B-ITAI Singapore
- Llama 3.3 Nemotron Super 49B v1.5 vs Qwen3 Omni 30B A3B ThinkingAlibaba (Qwen)
