GLM-4.5-Flash vs Gemini 2.5 Flash-Lite vs Llama 3.1 Nemotron Ultra 253B
Gemini 2.5 Flash-Lite comes out ahead, 85 to 65 and 65 on our weighted score.
Z.ai (Zhipu)GLM-4.5-Flash
65/100- ECI—
- PriceFree / Free
- Context131K
- Our pick
GoogleGemini 2.5 Flash-Lite
85/100- ECI133.9
- Price$0.10 / $0.40
- Context1.05M
NVIDIALlama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
Gemini 2.5 Flash-Lite is our pick
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 85/100 against GLM-4.5-Flash (65) and Llama 3.1 Nemotron Ultra 253B (65). It leads on inputs & features and context window. 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 · Gemini 2.5 Flash-Lite $0.175 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 Flash-LiteGemini 2.5 Flash-Lite 1,048,576 · GLM-4.5-Flash 131,072 · Llama 3.1 Nemotron Ultra 253B 128,000 tokens
- Widest inputsGemini 2.5 Flash-LiteGLM-4.5-Flash: Text · Gemini 2.5 Flash-Lite: Text, Images, PDFs, Audio, Video · Llama 3.1 Nemotron Ultra 253B: Text
- Self-hostingLlama 3.1 Nemotron Ultra 253BPublishes downloadable weights
| Measure | Weight | GLM-4.5-Flash | Gemini 2.5 Flash-Lite | Llama 3.1 Nemotron Ultra 253B |
|---|---|---|---|---|
| Price | 50% | 100 | 86 | 100 |
| Inputs & features | 30% | 35 | 100 | 35 |
| Context window | 20% | 24 | 61 | 24 |
| Overall | 100% | 65/100 | 85/100 | 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 | GLM-4.5-FlashZ.ai (Zhipu) | Gemini 2.5 Flash-LiteGoogle | Llama 3.1 Nemotron Ultra 253BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 133.9 | — |
| ECI rank | — | #118 of 148 | — |
| Price per million tokens | |||
| Input | Free (best) | $0.10 | Free (best) |
| Output | Free (best) | $0.40 | Free (best) |
| Cached input | — | $0.01 | — |
| Blended (3:1) | Free (best) | $0.175 | Free (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Google API | Official Nvidia API |
| Limits | |||
| Context window | 131,072 tokens | 1,048,576 tokens (best) | 128,000 tokens |
| Max output | 98,304 tokens (best) | 65,536 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | glm-4.5-flash | gemini-2.5-flash-lite | nvidia/llama-3.1-nemotron-ultra-253b-v1 |
| API providers | 4 | 20 (best) | 1 |
| Released | Jul 28, 2025 | Jun 17, 2025 | Apr 7, 2025 |
| Knowledge cutoff | Apr 2025 | Jan 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
- Gemini 2.5 Flash-Lite$1.80
- Llama 3.1 Nemotron Ultra 253BFree
Which should you choose?
Which is better: GLM-4.5-Flash, Gemini 2.5 Flash-Lite or Llama 3.1 Nemotron Ultra 253B?
Gemini 2.5 Flash-Lite is the better all-round choice, scoring 85/100 against GLM-4.5-Flash (65) and Llama 3.1 Nemotron Ultra 253B (65). It leads on inputs & features and context window. 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, Gemini 2.5 Flash-Lite or Llama 3.1 Nemotron Ultra 253B?
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); Gemini 2.5 Flash-Lite costs $0.10 input / $0.40 output per million tokens (official Google API price). 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, Gemini 2.5 Flash-Lite has an ECI of 133.9 and Llama 3.1 Nemotron Ultra 253B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5-Flash, Gemini 2.5 Flash-Lite and Llama 3.1 Nemotron Ultra 253B 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?
Gemini 2.5 Flash-Lite has the largest context window at 1,048,576 tokens, against 131,072 for GLM-4.5-Flash and 128,000 for Llama 3.1 Nemotron Ultra 253B. Maximum output per response: GLM-4.5-Flash up to 98,304, Gemini 2.5 Flash-Lite up to 65,536, Llama 3.1 Nemotron Ultra 253B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5-Flash accepts text; Gemini 2.5 Flash-Lite accepts text, images, PDFs, audio and video; Llama 3.1 Nemotron Ultra 253B accepts text. Gemini 2.5 Flash-Lite handles the widest range of inputs.
Are any of these open source?
Llama 3.1 Nemotron Ultra 253B publishes its weights and can be self-hosted; GLM-4.5-Flash and Gemini 2.5 Flash-Lite is proprietary.
Which is newer?
GLM-4.5-Flash is the newest, released Jul 28, 2025. Gemini 2.5 Flash-Lite came out Jun 17, 2025; Llama 3.1 Nemotron Ultra 253B came out Apr 7, 2025. Knowledge cutoff: GLM-4.5-Flash Apr 2025, Gemini 2.5 Flash-Lite Jan 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.
