Llama 3.1 Nemotron 70B Instruct vs Qwen-MT Turbo vs Solar Pro 2
Solar Pro 2 comes out ahead, 52 to 45 and 40 on our weighted score.
NVIDIALlama 3.1 Nemotron 70B Instruct
45/100- ECI—
- Price$0.478 / $0.504
- Context128K
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
- Our pick
Upstage
Solar Pro 2
52/100- ECI—
- Price$0.25 / $0.25
- Context66K
Solar Pro 2 is our pick
Solar Pro 2 is the better all-round choice, scoring 52/100 against Llama 3.1 Nemotron 70B Instruct (45) and Qwen-MT Turbo (40). It leads on inputs & features. Llama 3.1 Nemotron 70B Instruct 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 priceQwen-MT TurboQwen-MT Turbo $0.242 · Solar Pro 2 $0.25 · Llama 3.1 Nemotron 70B Instruct $0.485 per 1M tokens (3:1 blend)
- Longest contextLlama 3.1 Nemotron 70B InstructLlama 3.1 Nemotron 70B Instruct 128,000 · Solar Pro 2 65,536 · Qwen-MT Turbo 16,384 tokens
- Widest inputsSame inputsLlama 3.1 Nemotron 70B Instruct: Text · Qwen-MT Turbo: Text · Solar Pro 2: Text
- Self-hostingLlama 3.1 Nemotron 70B InstructPublishes downloadable weights
| Measure | Weight | Llama 3.1 Nemotron 70B Instruct | Qwen-MT Turbo | Solar Pro 2 |
|---|---|---|---|---|
| Price | 50% | 65 | 79 | 78 |
| Inputs & features | 30% | 25 | 0 | 35 |
| Context window | 20% | 24 | 0 | 12 |
| Overall | 100% | 45/100 | 40/100 | 52/100 |
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 | Llama 3.1 Nemotron 70B InstructNVIDIA | Qwen-MT TurboAlibaba (Qwen) | Solar Pro 2Upstage |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.478 | $0.16 (best) | $0.25 |
| Output | $0.504 | $0.49 | $0.25 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.485 | $0.242 (best) | $0.25 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Alibaba API | Official Upstage API |
| Limits | |||
| Context window | 128,000 tokens (best) | 16,384 tokens | 65,536 tokens |
| Max output | 8,192 tokens | 8,192 tokens | 8,192 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 | No | Yesminimal · high |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | nvidia/llama-3.1-nemotron-70b-instruct | qwen-mt-turbo | solar-pro2 |
| API providers | 3 (best) | 1 | 2 |
| Released | Apr 15, 2025 | Jan 2025 | May 20, 2025 |
| Knowledge cutoff | — | Apr 2024 | Mar 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama 3.1 Nemotron 70B Instruct$5.79
- Qwen-MT Turbo$2.58
- Solar Pro 2$3.00
Which should you choose?
Which is better: Llama 3.1 Nemotron 70B Instruct, Qwen-MT Turbo or Solar Pro 2?
Solar Pro 2 is the better all-round choice, scoring 52/100 against Llama 3.1 Nemotron 70B Instruct (45) and Qwen-MT Turbo (40). It leads on inputs & features. Llama 3.1 Nemotron 70B Instruct 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, Llama 3.1 Nemotron 70B Instruct, Qwen-MT Turbo or Solar Pro 2?
Qwen-MT Turbo is cheaper at $0.16 input / $0.49 output per million tokens (official Alibaba API price). Solar Pro 2 costs $0.25 input / $0.25 output per million tokens (official Upstage API price); Llama 3.1 Nemotron 70B Instruct costs $0.478 input / $0.504 output per million tokens (median across 2 API providers; free on Nvidia). At a typical mix of three input tokens to one output token, that is $0.242 per million tokens for Qwen-MT Turbo versus $0.25 for Solar Pro 2 (1× as much) and $0.485 for Llama 3.1 Nemotron 70B Instruct (2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama 3.1 Nemotron 70B Instruct has not been scored yet, Qwen-MT Turbo has not been scored yet and Solar Pro 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 3.1 Nemotron 70B Instruct, Qwen-MT Turbo and Solar Pro 2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-MT Turbo does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama 3.1 Nemotron 70B Instruct has the largest context window at 128,000 tokens, against 65,536 for Solar Pro 2 and 16,384 for Qwen-MT Turbo. Maximum output per response: Llama 3.1 Nemotron 70B Instruct up to 8,192, Qwen-MT Turbo up to 8,192, Solar Pro 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama 3.1 Nemotron 70B Instruct accepts text; Qwen-MT Turbo accepts text; Solar Pro 2 accepts text. They handle the same number of input types.
Are any of these open source?
Llama 3.1 Nemotron 70B Instruct publishes its weights and can be self-hosted; Qwen-MT Turbo and Solar Pro 2 is proprietary.
Which is newer?
Solar Pro 2 is the newest, released May 20, 2025. Llama 3.1 Nemotron 70B Instruct came out Apr 15, 2025; Qwen-MT Turbo came out Jan 2025. Knowledge cutoff: Qwen-MT Turbo Apr 2024, Solar Pro 2 Mar 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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