GLM-4.5-Flash vs Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1
Too close to call on our weighted score (GLM-4.5-Flash 65, Llama 3.1 Nemotron Ultra 253B 65, Llama 3.3 Nemotron Super 49B v1 60). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
NVIDIALlama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
- NVIDIA
Llama 3.3 Nemotron Super 49B v1
60/100- ECI—
- Price$0.15 / $0.15
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (GLM-4.5-Flash 65/100, Llama 3.1 Nemotron Ultra 253B 65/100, Llama 3.3 Nemotron Super 49B v1 60/100), so choose by what matters most for your work: GLM-4.5-Flash 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 priceGLM-4.5-Flash and Llama 3.1 Nemotron Ultra 253BGLM-4.5-Flash Free · Llama 3.1 Nemotron Ultra 253B Free · Llama 3.3 Nemotron Super 49B v1 $0.15 per 1M tokens (3:1 blend)
- Longest contextGLM-4.5-Flash and Llama 3.3 Nemotron Super 49B v1GLM-4.5-Flash 131,072 · Llama 3.3 Nemotron Super 49B v1 131,072 · Llama 3.1 Nemotron Ultra 253B 128,000 tokens
- Widest inputsSame inputsGLM-4.5-Flash: Text · Llama 3.1 Nemotron Ultra 253B: Text · Llama 3.3 Nemotron Super 49B v1: Text
- Self-hostingLlama 3.1 Nemotron Ultra 253B and Llama 3.3 Nemotron Super 49B v1Publishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Llama 3.1 Nemotron Ultra 253B | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 89 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 65/100 | 65/100 | 60/100 |
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 3.1 Nemotron Ultra 253BNVIDIA | Llama 3.3 Nemotron Super 49B v1NVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | Free (best) | Free (best) | $0.15 |
| Output | Free (best) | Free (best) | $0.15 |
| Cached input | — | — | — |
| Blended (3:1) | Free (best) | Free (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Nvidia 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 | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | glm-4.5-flash | nvidia/llama-3.1-nemotron-ultra-253b-v1 | nvidia/llama-3.3-nemotron-super-49b-v1 |
| API providers | 4 (best) | 1 | 2 |
| Released | Jul 28, 2025 | Apr 7, 2025 | Apr 7, 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 3.1 Nemotron Ultra 253BFree
- Llama 3.3 Nemotron Super 49B v1$1.80
Which should you choose?
Which is better: GLM-4.5-Flash, Llama 3.1 Nemotron Ultra 253B or Llama 3.3 Nemotron Super 49B v1?
It is close. Our weighted score puts them within a point (GLM-4.5-Flash 65/100, Llama 3.1 Nemotron Ultra 253B 65/100, Llama 3.3 Nemotron Super 49B v1 60/100), so choose by what matters most for your work: GLM-4.5-Flash 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-Flash, Llama 3.1 Nemotron Ultra 253B or Llama 3.3 Nemotron Super 49B v1?
GLM-4.5-Flash is cheaper at Free input / Free output per million tokens (official Z.AI API price). Llama 3.1 Nemotron Ultra 253B costs Free input / Free output per million tokens (official Nvidia 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). 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 3.1 Nemotron Ultra 253B has not been scored yet and Llama 3.3 Nemotron Super 49B v1 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Llama 3.1 Nemotron Ultra 253B and Llama 3.3 Nemotron Super 49B v1 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 have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Llama 3.1 Nemotron Ultra 253B. Maximum output per response: GLM-4.5-Flash up to 98,304, Llama 3.1 Nemotron Ultra 253B up to 8,192, Llama 3.3 Nemotron Super 49B v1 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Llama 3.1 Nemotron Ultra 253B accepts text; Llama 3.3 Nemotron Super 49B v1 accepts text. They handle the same number of input types.
Are any of these open source?
Llama 3.1 Nemotron Ultra 253B 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 3.1 Nemotron Ultra 253B came out Apr 7, 2025; Llama 3.3 Nemotron Super 49B v1 came out Apr 7, 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.
