Qwen2.5-VL 7B Instruct vs Llama 3.1 Nemotron Ultra 253B vs Codestral
Llama 3.1 Nemotron Ultra 253B comes out ahead, 65 to 51 and 48 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen2.5-VL 7B Instruct
51/100- ECI—
- Price$0.35 / $1.05
- Context131K
- Our pick
NVIDIALlama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
Mistral AICodestral
48/100- ECI—
- Price$0.30 / $0.90
- Context256K
Llama 3.1 Nemotron Ultra 253B is our pick
Llama 3.1 Nemotron Ultra 253B is the better all-round choice, scoring 65/100 against Qwen2.5-VL 7B Instruct (51) and Codestral (48). It leads on price. Qwen2.5-VL 7B Instruct wins on inputs & features. Codestral wins on 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 priceLlama 3.1 Nemotron Ultra 253BLlama 3.1 Nemotron Ultra 253B Free · Codestral $0.45 · Qwen2.5-VL 7B Instruct $0.525 per 1M tokens (3:1 blend)
- Longest contextCodestralCodestral 256,000 · Qwen2.5-VL 7B Instruct 131,072 · Llama 3.1 Nemotron Ultra 253B 128,000 tokens
- Widest inputsQwen2.5-VL 7B InstructQwen2.5-VL 7B Instruct: Text, Images · Llama 3.1 Nemotron Ultra 253B: Text · Codestral: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen2.5-VL 7B Instruct | Llama 3.1 Nemotron Ultra 253B | Codestral |
|---|---|---|---|---|
| Price | 50% | 63 | 100 | 66 |
| Inputs & features | 30% | 50 | 35 | 25 |
| Context window | 20% | 24 | 24 | 36 |
| Overall | 100% | 51/100 | 65/100 | 48/100 |
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 | Qwen2.5-VL 7B InstructAlibaba (Qwen) | Llama 3.1 Nemotron Ultra 253BNVIDIA | CodestralMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.35 | Free (best) | $0.30 |
| Output | $1.05 | Free (best) | $0.90 |
| Cached input | — | — | $0.03 |
| Blended (3:1) | $0.525 | Free (best) | $0.45 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Nvidia API | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens | 128,000 tokens | 256,000 tokens (best) |
| Max output | 8,192 tokens (best) | 8,192 tokens (best) | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen2-5-vl-7b-instruct | nvidia/llama-3.1-nemotron-ultra-253b-v1 | codestral-latest |
| API providers | 1 | 1 | 3 (best) |
| Released | Sep 2024 | Apr 7, 2025 | May 29, 2024 |
| Knowledge cutoff | Apr 2024 | — | Oct 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen2.5-VL 7B Instruct$5.60
- Llama 3.1 Nemotron Ultra 253BFree
- Codestral$4.80
Which should you choose?
Which is better: Qwen2.5-VL 7B Instruct, Llama 3.1 Nemotron Ultra 253B or Codestral?
Llama 3.1 Nemotron Ultra 253B is the better all-round choice, scoring 65/100 against Qwen2.5-VL 7B Instruct (51) and Codestral (48). It leads on price. Qwen2.5-VL 7B Instruct wins on inputs & features. Codestral wins on 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, Qwen2.5-VL 7B Instruct, Llama 3.1 Nemotron Ultra 253B or Codestral?
Llama 3.1 Nemotron Ultra 253B is cheaper at Free input / Free output per million tokens (official Nvidia API price). Codestral costs $0.30 input / $0.90 output per million tokens (official Mistral API price); Qwen2.5-VL 7B Instruct costs $0.35 input / $1.05 output per million tokens (official Alibaba API price). Llama 3.1 Nemotron Ultra 253B is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen2.5-VL 7B Instruct has not been scored yet, Llama 3.1 Nemotron Ultra 253B has not been scored yet and Codestral has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5-VL 7B Instruct, Llama 3.1 Nemotron Ultra 253B and Codestral 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?
Codestral has the largest context window at 256,000 tokens, against 131,072 for Qwen2.5-VL 7B Instruct and 128,000 for Llama 3.1 Nemotron Ultra 253B. Maximum output per response: Qwen2.5-VL 7B Instruct up to 8,192, Llama 3.1 Nemotron Ultra 253B up to 8,192, Codestral up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5-VL 7B Instruct accepts text and images; Llama 3.1 Nemotron Ultra 253B accepts text; Codestral accepts text. Qwen2.5-VL 7B Instruct 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?
Llama 3.1 Nemotron Ultra 253B is the newest, released Apr 7, 2025. Qwen2.5-VL 7B Instruct came out Sep 2024; Codestral came out May 29, 2024. Knowledge cutoff: Qwen2.5-VL 7B Instruct Apr 2024, Codestral Oct 2024.
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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