Nemotron Mini 4B Instruct vs Llama 3.3 Nemotron Super 49B v1 vs GLM-4.5-Flash
GLM-4.5-Flash comes out ahead, 65 to 62 and 60 on our weighted score.
NVIDIANemotron Mini 4B Instruct
62/100- ECI—
- PriceFree / Free
- Context128K
- NVIDIA
Llama 3.3 Nemotron Super 49B v1
60/100- ECI—
- Price$0.15 / $0.15
- Context131K
- Our pick
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
GLM-4.5-Flash is our pick
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Nemotron Mini 4B Instruct (62) and Llama 3.3 Nemotron Super 49B v1 (60). 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 priceNemotron Mini 4B Instruct and GLM-4.5-FlashNemotron Mini 4B Instruct Free · GLM-4.5-Flash Free · Llama 3.3 Nemotron Super 49B v1 $0.15 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1 and GLM-4.5-FlashLlama 3.3 Nemotron Super 49B v1 131,072 · GLM-4.5-Flash 131,072 · Nemotron Mini 4B Instruct 128,000 tokens
- Widest inputsSame inputsNemotron Mini 4B Instruct: Text · Llama 3.3 Nemotron Super 49B v1: Text · GLM-4.5-Flash: Text
- Self-hostingNemotron Mini 4B Instruct and Llama 3.3 Nemotron Super 49B v1Publishes downloadable weights
| Measure | Weight | Nemotron Mini 4B Instruct | Llama 3.3 Nemotron Super 49B v1 | GLM-4.5-Flash |
|---|---|---|---|---|
| Price | 50% | 100 | 89 | 100 |
| Inputs & features | 30% | 25 | 35 | 35 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 60/100 | 65/100 |
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 | Nemotron Mini 4B InstructNVIDIA | Llama 3.3 Nemotron Super 49B v1NVIDIA | GLM-4.5-FlashZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | $0.15 | Free (best) |
| Output | Free (best) | $0.15 | Free (best) |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | $0.15 | Free (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Nvidia API | Median of 1 providers | Official Z.AI API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 8,192 tokens | 131,072 tokens (best) | 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 | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | nvidia/nemotron-mini-4b-instruct | nvidia/llama-3.3-nemotron-super-49b-v1 | glm-4.5-flash |
| API providers | 1 | 2 | 4 (best) |
| Released | Aug 21, 2024 | Apr 7, 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.
- Nemotron Mini 4B InstructFree
- Llama 3.3 Nemotron Super 49B v1$1.80
- GLM-4.5-FlashFree
Which should you choose?
Which is better: Nemotron Mini 4B Instruct, Llama 3.3 Nemotron Super 49B v1 or GLM-4.5-Flash?
GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Nemotron Mini 4B Instruct (62) and Llama 3.3 Nemotron Super 49B v1 (60). 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, Nemotron Mini 4B Instruct, Llama 3.3 Nemotron Super 49B v1 or GLM-4.5-Flash?
Nemotron Mini 4B Instruct is cheaper at Free input / Free output per million tokens (official Nvidia API price). GLM-4.5-Flash costs Free input / Free output per million tokens (official Z.AI API price); Llama 3.3 Nemotron Super 49B v1 costs $0.15 input / $0.15 output per million tokens (median across 1 API provider; free on Nvidia). Nemotron Mini 4B Instruct is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Nemotron Mini 4B Instruct has not been scored yet, Llama 3.3 Nemotron Super 49B v1 has not been scored yet and GLM-4.5-Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Nemotron Mini 4B Instruct, Llama 3.3 Nemotron Super 49B v1 and GLM-4.5-Flash 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 and GLM-4.5-Flash have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Nemotron Mini 4B Instruct. Maximum output per response: Nemotron Mini 4B Instruct up to 8,192, Llama 3.3 Nemotron Super 49B v1 up to 131,072, GLM-4.5-Flash up to 98,304 tokens.
Which can read images, PDFs, audio or video?
Nemotron Mini 4B Instruct accepts text; Llama 3.3 Nemotron Super 49B v1 accepts text; GLM-4.5-Flash accepts text. They handle the same number of input types.
Are any of these open source?
Nemotron Mini 4B Instruct and Llama 3.3 Nemotron Super 49B v1 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 came out Apr 7, 2025; Nemotron Mini 4B Instruct came out Aug 21, 2024. 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.
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