GLM-4.5-Flash vs Llama 3.3 Nemotron Super 49B v1.5 vs Mistral Nemotron
GLM-4.5-Flash comes out ahead, 65 to 62 and 50 on our weighted score.
- Our pick
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
NVIDIALlama 3.3 Nemotron Super 49B v1.5
50/100- ECI—
- Price$0.40 / $0.40
- Context131K
- NVIDIA
Mistral Nemotron
62/100- ECI—
- PriceFree / Free
- Context128K
GLM-4.5-Flash is our pick
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Mistral Nemotron (62) and Llama 3.3 Nemotron Super 49B v1.5 (50). 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 priceGLM-4.5-Flash and Mistral NemotronGLM-4.5-Flash Free · Mistral Nemotron Free · Llama 3.3 Nemotron Super 49B v1.5 $0.40 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-Flash and Llama 3.3 Nemotron Super 49B v1.5GLM-4.5-Flash 131,072 · Llama 3.3 Nemotron Super 49B v1.5 131,072 · Mistral Nemotron 128,000 tokens
- Widest inputsSame inputsGLM-4.5-Flash: Text · Llama 3.3 Nemotron Super 49B v1.5: Text · Mistral Nemotron: Text
- Self-hostingLlama 3.3 Nemotron Super 49B v1.5 and Mistral NemotronPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Llama 3.3 Nemotron Super 49B v1.5 | Mistral Nemotron |
|---|---|---|---|---|
| Price | 50% | 100 | 69 | 100 |
| Inputs & features | 30% | 35 | 35 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 65/100 | 50/100 | 62/100 |
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-FlashZ.ai (Zhipu) | Llama 3.3 Nemotron Super 49B v1.5NVIDIA | Mistral NemotronNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | $0.40 | Free (best) |
| Output | Free (best) | $0.40 | Free (best) |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | $0.40 | Free (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 1 providers | Official Nvidia API |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 98,304 tokens | 131,072 tokens (best) | 8,192 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 | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | glm-4.5-flash | nvidia/llama-3.3-nemotron-super-49b-v1.5 | mistralai/mistral-nemotron |
| API providers | 4 (best) | 2 | 1 |
| Released | Jul 28, 2025 | Jul 25, 2025 | Jun 11, 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-FlashFree
- Llama 3.3 Nemotron Super 49B v1.5$4.80
- Mistral NemotronFree
Which should you choose?
Which is better: GLM-4.5-Flash, Llama 3.3 Nemotron Super 49B v1.5 or Mistral Nemotron?
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Mistral Nemotron (62) and Llama 3.3 Nemotron Super 49B v1.5 (50). 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-Flash, Llama 3.3 Nemotron Super 49B v1.5 or Mistral Nemotron?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Mistral Nemotron costs Free input / Free output per million tokens (official Nvidia API price); Llama 3.3 Nemotron Super 49B v1.5 costs $0.40 input / $0.40 output per million tokens (median across 1 API provider; free on Nvidia). GLM-4.5-Flash is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5-Flash has not been scored yet, Llama 3.3 Nemotron Super 49B v1.5 has not been scored yet and Mistral Nemotron has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Llama 3.3 Nemotron Super 49B v1.5 and Mistral Nemotron 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-Flash and Llama 3.3 Nemotron Super 49B v1.5 have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemotron. Maximum output per response: GLM-4.5-Flash up to 98,304, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072, Mistral Nemotron up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Llama 3.3 Nemotron Super 49B v1.5 accepts text; Mistral Nemotron 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 Mistral Nemotron publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.
Which is newer?
GLM-4.5-Flash is the newest, released Jul 28, 2025. Llama 3.3 Nemotron Super 49B v1.5 came out Jul 25, 2025; Mistral Nemotron came out Jun 11, 2025. Knowledge cutoff: GLM-4.5-Flash 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.
