How do two compact multimodal deployments compare on document tasks?
This page compares the older editions named in its title. For current GPT-6, Claude 5.5 and Gemini 3.8 comparisons, open the current comparison library. The historical model identities are preserved for migration and reproducibility.
An instruction-tuned model for text and image understanding.
Vision support and quantization compatibility depend on the serving implementation. Test the exact model artifact.
Official source ↗An instruction-tuned open-weight model with text and image inputs.
Gemma and Gemini are separate products. Check Gemma license terms and the runtime used for image inputs.
Official source ↗Exact checkpoint fields from pinned publisher files. The configured position limit does not establish the context enabled by a serving endpoint.
| Configuration field | Mistral Small 3.1 | Gemma 3 27B |
|---|---|---|
| Published architecture | Mistral3ForConditionalGeneration | Not established |
| Transformer layers | 40 | Not established |
| Configured position limit | 131,072 | Not established |
| Pinned configuration revision | 68faf511d618 | Not established |
| Configuration checked | 2026-10-10 | Not established |
| Original configuration | Read pinned file ↗ | Read publisher source ↗ |
Use identical documents with a known ground truth. Track incorrect values independently from invalid JSON. Neither model's vision capability proves that your chosen serving endpoint accepts the same image formats.
Use the same representative inputs and a written answer rubric. Pin the model version and record the endpoint, thinking configuration, quantization when known, context length, output allowance and concurrency. Preserve native request differences in an adapter rather than silently dropping fields.
Measure full request time, error rate and reported usage. For tools, validate arguments and score the result after execution. For images, preserve resolution and preprocessing. A valid response format alone does not establish a correct answer.
Calculate input cost plus all billed output, including reasoning where the provider counts it. Add retries and discarded answers. Divide the total by the number of results that pass your quality criteria. Keep API usage cost separate from deployment and engineering cost.
Estimate token costs with the API calculator →Enter results from your own evaluation. A task should count once it meets your written quality criteria. Include failed attempts and retries in total spend. These inputs stay in this browser and are not a Flonno benchmark.
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
A source benchmark can inform your shortlist, while your evaluation shows whether a model finishes the work at an acceptable cost. Keep the chosen endpoint, processing terms and output limits in the decision record. Inclusion in the Radar does not mean the model is served by Flonno.
For the API currently offered here, explore Qwen3.8 Flash 27B and the production evaluation guide.
Is a smaller vision-language model sufficient for your extraction task?
Do you need text reasoning, image inputs, or both?
Which open-weight multimodal system matches your deployment constraints?
