GLM-4.5V vs Llama 3.1 Nemotron 70B Instruct vs Qwen3-Next 80B-A3B Instruct
GLM-4.5V comes out ahead, 49 to 45 and 39 on our weighted score, though Llama 3.1 Nemotron 70B Instruct is 46% cheaper per token.
- Our pick
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
Alibaba (Qwen)Qwen3-Next 80B-A3B Instruct
39/100- ECI—
- Price$0.50 / $2.00
- Context131K
GLM-4.5V is our pick
GLM-4.5V is the better all-round choice, scoring 49/100 against Llama 3.1 Nemotron 70B Instruct (45) and Qwen3-Next 80B-A3B Instruct (39). It leads on inputs & features. Llama 3.1 Nemotron 70B Instruct wins 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 3.1 Nemotron 70B InstructLlama 3.1 Nemotron 70B Instruct $0.485 · Qwen3-Next 80B-A3B Instruct $0.875 · GLM-4.5V $0.90 per 1M tokens (3:1 blend)
- Longest contextQwen3-Next 80B-A3B InstructQwen3-Next 80B-A3B Instruct 131,072 · Llama 3.1 Nemotron 70B Instruct 128,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5VGLM-4.5V: Text, Images, Video · Llama 3.1 Nemotron 70B Instruct: Text · Qwen3-Next 80B-A3B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.5V | Llama 3.1 Nemotron 70B Instruct | Qwen3-Next 80B-A3B Instruct |
|---|---|---|---|---|
| Price | 50% | 52 | 65 | 53 |
| Inputs & features | 30% | 70 | 25 | 25 |
| Context window | 20% | 12 | 24 | 24 |
| Overall | 100% | 49/100 | 45/100 | 39/100 |
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-4.5VZ.ai (Zhipu) | Llama 3.1 Nemotron 70B InstructNVIDIA | Qwen3-Next 80B-A3B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.478 (best) | $0.50 |
| Output | $1.80 | $0.504 (best) | $2.00 |
| Cached input | — | — | — |
| Blended (3:1) | $0.90 | $0.485 (best) | $0.875 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 2 providers | Official Alibaba API |
| Limits | |||
| Context window | 64,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 16,384 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.5v | nvidia/llama-3.1-nemotron-70b-instruct | qwen3-next-80b-a3b-instruct |
| API providers | 11 | 3 | 13 (best) |
| Released | Aug 11, 2025 | Apr 15, 2025 | Sep 2025 |
| Knowledge cutoff | Apr 2025 | — | 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-4.5V$9.60
- Llama 3.1 Nemotron 70B Instruct$5.79
- Qwen3-Next 80B-A3B Instruct$9.00
Which should you choose?
Which is better: GLM-4.5V, Llama 3.1 Nemotron 70B Instruct or Qwen3-Next 80B-A3B Instruct?
GLM-4.5V is the better all-round choice, scoring 49/100 against Llama 3.1 Nemotron 70B Instruct (45) and Qwen3-Next 80B-A3B Instruct (39). It leads on inputs & features. Llama 3.1 Nemotron 70B Instruct wins 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-4.5V, Llama 3.1 Nemotron 70B Instruct or Qwen3-Next 80B-A3B Instruct?
Llama 3.1 Nemotron 70B Instruct is cheaper at $0.478 input / $0.504 output per million tokens (median across 2 API providers; free on Nvidia). Qwen3-Next 80B-A3B Instruct costs $0.50 input / $2.00 output per million tokens (official Alibaba API price); GLM-4.5V costs $0.60 input / $1.80 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.485 per million tokens for Llama 3.1 Nemotron 70B Instruct versus $0.875 for Qwen3-Next 80B-A3B Instruct (1.8× as much) and $0.90 for GLM-4.5V (1.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.5V has not been scored yet, Llama 3.1 Nemotron 70B Instruct has not been scored yet and Qwen3-Next 80B-A3B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.5V, Llama 3.1 Nemotron 70B Instruct and Qwen3-Next 80B-A3B Instruct 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?
Qwen3-Next 80B-A3B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama 3.1 Nemotron 70B Instruct and 64,000 for GLM-4.5V. Maximum output per response: GLM-4.5V up to 16,384, Llama 3.1 Nemotron 70B Instruct up to 8,192, Qwen3-Next 80B-A3B Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-4.5V accepts text, images and video; Llama 3.1 Nemotron 70B Instruct accepts text; Qwen3-Next 80B-A3B Instruct accepts text. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3-Next 80B-A3B Instruct is the newest, released Sep 2025. GLM-4.5V came out Aug 11, 2025; Llama 3.1 Nemotron 70B Instruct came out Apr 15, 2025. Knowledge cutoff: GLM-4.5V Apr 2025, Qwen3-Next 80B-A3B Instruct 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.
