Llama-3.2-90B-Vision-Instruct vs Llama-3.2-1B
Llama-3.2-90B-Vision-Instruct comes out ahead, 28 to 11 on our weighted score.
- Our pick
MetaLlama-3.2-90B-Vision-Instruct
28/100- ECI125.5
- Price—
- Context128K
- Meta
Llama-3.2-1B
11/100- ECI—
- Price$0.027 / $0.201
- Context131K
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Llama-3.2-90B-Vision-Instruct is our pick
Llama-3.2-90B-Vision-Instruct is the better all-round choice, scoring 28/100 against Llama-3.2-1B (11). It leads on capability and inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
- CapabilityLlama-3.2-90B-Vision-InstructShared benchmarks: Llama-3.2-90B-Vision-Instruct 21.8% · Llama-3.2-1B 12.2%
- Lowest priceLlama-3.2-1BLlama-3.2-1B $0.07 per 1M tokens (3:1 blend) · Llama-3.2-90B-Vision-Instruct unpriced
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Llama-3.2-90B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-90B-Vision-InstructLlama-3.2-90B-Vision-Instruct: Text, Images · Llama-3.2-1B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-90B-Vision-Instruct | Llama-3.2-1B |
|---|---|---|---|
| CapabilityShared benchmarks | 67% | 22 | 12 |
| Inputs & features | 20% | 50 | 0 |
| Context window | 13% | 24 | 25 |
| Overall | 100% | 28/100 | 11/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.2-90B-Vision-InstructMeta | Llama-3.2-1BMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 125.5 | — |
| ECI rank | #150 of 165 | — |
| GPQA DiamondGraduate-level science questions | 41.0% (best) | 23.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 2.6% (best) | 0.6% |
| Price per million tokens | ||
| Input | — | $0.027 |
| Output | — | $0.201 |
| Cached input | — | — |
| Blended (3:1) | — | $0.07 |
| Long-context rate | — | Same rate |
| Price source | — | Median of 6 providers |
| Limits | ||
| Context window | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,096 tokens | 8,192 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | OpenLlama 3.2 Community License | OpenLlama 3.2 Community License |
| API model ID | — | — |
| API providers | 1 | 6 (best) |
| Released | Sep 25, 2024 | Sep 25, 2024 |
| Knowledge cutoff | Dec 2023 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.2-90B-Vision-Instruct—
- Llama-3.2-1B$0.672
Which should you choose?
Which is better: Llama-3.2-90B-Vision-Instruct or Llama-3.2-1B?
Llama-3.2-90B-Vision-Instruct is the better all-round choice, scoring 28/100 against Llama-3.2-1B (11). It leads on capability and inputs & features. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
Which is cheaper, Llama-3.2-90B-Vision-Instruct or Llama-3.2-1B?
Llama-3.2-1B is cheaper at $0.027 input / $0.201 output per million tokens (median across 6 API providers). . At a typical mix of three input tokens to one output token, that is $0.07 per million tokens for Llama-3.2-1B versus . Llama-3.2-90B-Vision-Instruct has no published per-token price.
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): Llama-3.2-90B-Vision-Instruct 21.8% and Llama-3.2-1B 12.2%. On individual benchmarks: GPQA Diamond — Llama-3.2-90B-Vision-Instruct 41.0%, Llama-3.2-1B 23.9%; OTIS Mock AIME 2024–2025 — Llama-3.2-90B-Vision-Instruct 2.6%, Llama-3.2-1B 0.6%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-90B-Vision-Instruct and Llama-3.2-1B yet, so there is no like-for-like coding score. On overall capability, Llama-3.2-90B-Vision-Instruct leads, which tends to carry over to coding, but test on your own codebase. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B has the largest context window at 131,072 tokens, against 128,000 for Llama-3.2-90B-Vision-Instruct. Maximum output per response: Llama-3.2-90B-Vision-Instruct up to 4,096, Llama-3.2-1B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-90B-Vision-Instruct accepts text and images; Llama-3.2-1B accepts text. Llama-3.2-90B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Llama 3.2 Community License), so you can self-host them.
Which is newer?
Llama-3.2-90B-Vision-Instruct is the newest, released Sep 25, 2024. Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-90B-Vision-Instruct Dec 2023, Llama-3.2-1B Dec 2023.
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 Nov 2, 2026.
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