Llama-3.2-11B-Vision-Instruct vs Nova Lite vs Qwen-MT Turbo
Nova Lite comes out ahead, 77 to 58 and 40 on our weighted score, and it is the cheaper option too.
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
- Our pick
AmazonNova Lite
77/100- ECI—
- Price$0.06 / $0.24
- Context300K
Alibaba (Qwen)Qwen-MT Turbo
40/100- ECI—
- Price$0.16 / $0.49
- Context16K
Nova Lite is our pick
Nova Lite is the better all-round choice, scoring 77/100 against Llama-3.2-11B-Vision-Instruct (58) and Qwen-MT Turbo (40). It leads on price, 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 priceNova LiteNova Lite $0.105 · Qwen-MT Turbo $0.242 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextNova LiteNova Lite 300,000 · Llama-3.2-11B-Vision-Instruct 128,000 · Qwen-MT Turbo 16,384 tokens
- Widest inputsNova LiteLlama-3.2-11B-Vision-Instruct: Text, Images · Nova Lite: Text, Images, PDFs, Video · Qwen-MT Turbo: Text
- Self-hostingLlama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Llama-3.2-11B-Vision-Instruct | Nova Lite | Qwen-MT Turbo |
|---|---|---|---|---|
| Price | 50% | 76 | 96 | 79 |
| Inputs & features | 30% | 50 | 70 | 0 |
| Context window | 20% | 24 | 39 | 0 |
| Overall | 100% | 58/100 | 77/100 | 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 | Nova LiteAmazon | Qwen-MT TurboAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.197 | $0.06 (best) | $0.16 |
| Output | $0.51 | $0.24 (best) | $0.49 |
| Cached input | — | $0.015 | — |
| Blended (3:1) | $0.275 | $0.105 (best) | $0.242 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Amazon Bedrock API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 300,000 tokens (best) | 16,384 tokens |
| Max output | 4,096 tokens | 10,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | — | amazon.nova-lite-v1:0 | qwen-mt-turbo |
| API providers | 2 | 3 (best) | 1 |
| Released | Sep 25, 2024 | Dec 3, 2024 | Jan 2025 |
| Knowledge cutoff | Dec 2023 | Oct 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
- Nova Lite$1.08
- Qwen-MT Turbo$2.58
Which should you choose?
Which is better: Llama-3.2-11B-Vision-Instruct, Nova Lite or Qwen-MT Turbo?
Nova Lite is the better all-round choice, scoring 77/100 against Llama-3.2-11B-Vision-Instruct (58) and Qwen-MT Turbo (40). It leads on price, 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, Llama-3.2-11B-Vision-Instruct, Nova Lite or Qwen-MT Turbo?
Nova Lite is cheaper at $0.06 input / $0.24 output per million tokens (official Amazon Bedrock API price). Qwen-MT Turbo costs $0.16 input / $0.49 output per million tokens (official Alibaba 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 $0.105 per million tokens for Nova Lite versus $0.242 for Qwen-MT Turbo (2.3× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (2.6× 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, Nova Lite 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, Nova Lite 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?
Nova Lite has the largest context window at 300,000 tokens, against 128,000 for Llama-3.2-11B-Vision-Instruct and 16,384 for Qwen-MT Turbo. Maximum output per response: Llama-3.2-11B-Vision-Instruct up to 4,096, Nova Lite up to 10,000, 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; Nova Lite accepts text, images, PDFs and video; Qwen-MT Turbo accepts text. Nova Lite handles the widest range of inputs.
Are any of these open source?
Llama-3.2-11B-Vision-Instruct publishes its weights and can be self-hosted; Nova Lite and Qwen-MT Turbo is proprietary.
Which is newer?
Qwen-MT Turbo is the newest, released Jan 2025. Nova Lite came out Dec 3, 2024; Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-11B-Vision-Instruct Dec 2023, Nova Lite Oct 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.
