Llama-3.2-11B-Vision-Instruct vs Command R vs Qwen-MT Turbo
Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 51 and 40 on our weighted score, though Qwen-MT Turbo is 12% cheaper per token.
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
Llama-3.2-11B-Vision-Instruct is our pick
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Qwen-MT Turbo (40). It leads on inputs & features. Qwen-MT Turbo 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 priceQwen-MT TurboQwen-MT Turbo $0.242 · Command R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-11B-Vision-Instruct and Command RLlama-3.2-11B-Vision-Instruct 128,000 · Command R 128,000 · Qwen-MT Turbo 16,384 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructLlama-3.2-11B-Vision-Instruct: Text, Images · Command R: Text · Qwen-MT Turbo: Text
- Self-hostingLlama-3.2-11B-Vision-Instruct and Command RPublishes downloadable weights
| Measure | Weight | Llama-3.2-11B-Vision-Instruct | Command R | Qwen-MT Turbo |
|---|---|---|---|---|
| Price | 50% | 76 | 77 | 79 |
| Inputs & features | 30% | 50 | 25 | 0 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 58/100 | 51/100 | 40/100 |
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-3.2-11B-Vision-InstructMeta | Command RCohere | Qwen-MT TurboAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.197 | $0.15 (best) | $0.16 |
| Output | $0.51 | $0.60 | $0.49 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.275 | $0.263 | $0.242 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Cohere API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens (best) | 128,000 tokens (best) | 16,384 tokens |
| Max output | 4,096 tokens | 4,000 tokens | 8,192 tokens (best) |
| 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 | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | command-r-08-2024 | qwen-mt-turbo |
| API providers | 2 | 5 (best) | 1 |
| Released | Sep 25, 2024 | Aug 30, 2024 | Jan 2025 |
| Knowledge cutoff | Dec 2023 | Jun 1, 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.2-11B-Vision-Instruct$2.99
- Command R$2.70
- Qwen-MT Turbo$2.58
Which should you choose?
Which is better: Llama-3.2-11B-Vision-Instruct, Command R or Qwen-MT Turbo?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Qwen-MT Turbo (40). It leads on inputs & features. Qwen-MT Turbo 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, Llama-3.2-11B-Vision-Instruct, Command R or Qwen-MT Turbo?
Qwen-MT Turbo is cheaper at $0.16 input / $0.49 output per million tokens (official Alibaba API price). Command R costs $0.15 input / $0.60 output per million tokens (official Cohere API price); Llama-3.2-11B-Vision-Instruct costs $0.197 input / $0.51 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.242 per million tokens for Qwen-MT Turbo versus $0.263 for Command R (1.1× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-11B-Vision-Instruct has not been scored yet, Command R has not been scored yet and Qwen-MT Turbo has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-11B-Vision-Instruct, Command R and Qwen-MT Turbo 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-3.2-11B-Vision-Instruct and Command R have the largest context windows (128,000 and 128,000 tokens), against 16,384 for Qwen-MT Turbo. Maximum output per response: Llama-3.2-11B-Vision-Instruct up to 4,096, Command R up to 4,000, Qwen-MT Turbo up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-11B-Vision-Instruct accepts text and images; Command R accepts text; Qwen-MT Turbo accepts text. Llama-3.2-11B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Llama-3.2-11B-Vision-Instruct and Command R publishes its weights and can be self-hosted; Qwen-MT Turbo is proprietary.
Which is newer?
Qwen-MT Turbo is the newest, released Jan 2025. Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024; Command R came out Aug 30, 2024. Knowledge cutoff: Llama-3.2-11B-Vision-Instruct Dec 2023, Command R Jun 1, 2024, Qwen-MT Turbo Apr 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.
