Vision language models
273 language models that take images as input: describe photos, read charts and documents, answer questions about screenshots.
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 Qwen3.6 35B-A3BAlibabaApache 2.0Prompt caching+4 | $0.95/1Mvia Deep Infra | 262K | $0.375/1M | $2.25/1M | — | 11K | |
 Gemini 2.5 FlashGooglePrompt cachingJSON+3 | $2/1Mvia APIMart | 1.05M | — | $2.50/1M | 1417 | 11K | |
| Kimi K2.6Moonshot AIOpen weights · with termsPrompt caching+4 | $2.40/1Mvia APIYI | 262K | $0.903/1M | $3.75/1M | 1509 | 11K | |
 MiMo-V2.5XiaomiMITPrompt caching+4 | $0.224/1Mvia CometAPI | 1.05M | $0.112/1M | $0.224/1M | 1438 | 10K | |
 GLM OCRZhipu AIMITVision | $0.028/1Mvia AIHubMix | 32K | $0.03/1M | $0.03/1M | — | 10K | |
| Qwen3.8 Omni FlashAlibabaPrompt cachingJSON+3 | $0.38/1Mvia AIHubMix | 1M | $0.113/1M | $0.382/1M | — | 10K | |
 Llama 4 MaverickMetaJSONTool calling+1 | $0.20/1Mvia AIHubMix | 1.05M | $0.20/1M | $0.20/1M | — | 9K | |
 Gemma 4 26B A4BorcarouterJSONReasoning+2 | $2.90/1Mvia OrcaRouter | 262K | $0.25/1M | $2.90/1M | — | 8K | |
| Llama 4 Scout 17B InstructMetaJSONTool calling+1 | $0.20/1Mvia AIHubMix | 1.31M | $0.20/1M | $0.20/1M | — | 7K | |
| Qwen3.5 4BAlibabaApache 2.0Prompt caching+3 | $0.07/1Mvia EmpirioLabs AI | 262K | $0.04/1M | $0.07/1M | — | 7K | |
| GLM 5.3 FlashxZhipu AIPrompt cachingJSON+3 | $0.987/1Mvia AIHubMix | 1.05M | $0.282/1M | $0.987/1M | — | 7K | |
| Gemma 4GoogleApache 2.0JSON+3 | $0.30/1Mvia FastRouter | 262K | $0.10/1M | $0.30/1M | — | 6K | |
| Qwen3-VL-8B-InstructAlibabaApache 2.0Prompt caching+3 | $0.278/1Mvia SiliconFlow | 262K | $0.069/1M | $0.278/1M | — | 6K | |
| Kimi K2.7 Code HighSpeedMoonshot AIOpen weights · with termsPrompt caching+4 | $2.76/1Mvia Kenari | 262K | $0.903/1M | $3.75/1M | 1474 | 6K | |
| Qwen3-VL-8B-ThinkingAlibabaApache 2.0JSON+3 | $0.694/1Mvia SiliconFlow | 131K | $0.069/1M | $0.694/1M | — | 6K | |
| Kimi K2.8 PreviewMoonshot AIPrompt cachingReasoning+1 | $3.91/1Mvia CrazyRouter | 1.05M | $0.942/1M | $3.91/1M | — | 5K | |
| Qwen3.6 27BAlibabaApache 2.0JSON+3 | $2/1Mvia OpenRouter | 262K | $0.60/1M | $3.60/1M | — | 4K | |
| Gemma 3 4B ITGoogleGemmaPrompt caching+2 | $0.05/1Mvia OpenRouter | 131K | $0.04/1M | $0.05/1M | 1291 | 4K | |
| Grok 4.5xAIPrompt cachingJSON+2 | $3.20/1Mvia APIMart | 500K | $2/1M | $6/1M | 1553 | 3K | |
 Mistral Medium 3.5MistralJSONReasoning+2 | $7.50/1Mvia Azure | 262K | $1.50/1M | $7.50/1M | 1421 | 3K | |
| Qwen3.5 397B-A17BAlibabaApache 2.0JSON+3 | $0.986/1Mvia AIHubMix | 262K | $0.172/1M | $1.03/1M | 1438 | 3K | |
| Qwen3.5 2BAlibabaApache 2.0JSON+3 | $0.10/1Mvia Requesty | 262K | $0.02/1M | $0.10/1M | — | 3K | |
| Qwen3.5 35B-A3BAlibabaApache 2.0JSON+3 | $0.451/1Mvia AIHubMix | 262K | $0.25/1M | $2/1M | 1395 | 3K | |
| Gemma 3 27BGoogleGemmaJSON+2 | $0.16/1Mvia Deep Infra | 131K | $0.08/1M | $0.16/1M | 1358 | 3K | |
| Grok Code Fast 1xAIPrompt cachingJSON+2 | $1.43/1Mvia Jiekou.AI | 256K | $0.19/1M | $1.43/1M | 1167 | 3K | |
| Qwen3.8 Max 0902AlibabaPrompt cachingJSON+3 | $4.11/1Mvia APIMart | 1M | $2/1M | $6/1M | — | 3K | |
| Gemma 4 26B A4B ITGoogleApache 2.0Prompt caching+4 | $0.225/1Mvia OpenRouter | 262K | $0.15/1M | $0.60/1M | — | 3K | |
| Qwen3.7 PlusAlibabaPrompt cachingJSON+3 | $0.30/1Mvia CrazyRouter | 1M | $0.276/1M | $1.10/1M | 1455 | 2K | |
| Qwen3.5 27BAlibabaApache 2.0JSON+3 | $0.677/1Mvia AIHubMix | 262K | $0.30/1M | $2.40/1M | 1409 | 2K | |
| Qwen 3.8 Max PrimeAlibabaPrompt cachingJSON+3 | $12/1Mvia OpenRouter | 1M | $4/1M | $12/1M | — | 2K | |
| Gemma 3 12B ITGoogleGemmaJSON+2 | $0.10/1Mvia Helicone | 131K | $0.05/1M | $0.10/1M | 1334 | 2K | |
| Ministral 3BMistralPrompt cachingJSON+2 | $0.10/1Mvia DevPass (LLM Gateway) | 131K | $0.10/1M | $0.10/1M | — | 2K | |
| GLM-4.6VZhipu AIMITPrompt caching+4 | $0.411/1Mvia AIHubMix | 131K | $0.30/1M | $0.90/1M | 1376 | 1K | |
 ERNIE 5.0BaiduPrompt cachingReasoning+1 | $3.29/1Mvia AIHubMix | 119K | $0.822/1M | $3.29/1M | — | 1K | |
 Fugu MaxSakana AIPrompt cachingJSON+3 | $6/1Mvia EmpirioLabs AI | 1M | $2/1M | $6/1M | — | 1K | |
| Mistral Small 4MistralPrompt cachingJSON+3 | $0.60/1Mvia EmpirioLabs AI | 262K | $0.15/1M | $0.60/1M | — | 930 | |
 Nemotron 3 Nano OmniNVIDIAOpen weights · with termsReasoning+2 | Free/1Mvia Kenari | 256K | Free/1M | Free/1M | — | 670 | |
 Decider V1.2 27BPerplexityApache 2.0Vision | Free/1Mvia Vercel AI Gateway | 262K | $0.02/1M | Free/1M | — | 470 | |
| Ministral 8BMistralPrompt cachingJSON+2 | $0.143/1Mvia AI/ML API | 262K | $0.143/1M | $0.143/1M | 1191 | 340 | |
| Ministral 14B (Arkanecloud)MistralPrompt cachingJSON+2 | $0.20/1Mvia Vercel AI Gateway | 262K | $0.20/1M | $0.20/1M | — | 270 | |
 Doubao-Seed-CodeByteDancePrompt cachingReasoning+1 | $1.12/1Mvia ZenMux | 256K | $0.17/1M | $1.12/1M | — | 30 | |
 Ahm Phi 3.5 Vision InstructMicrosoftMITVision | $1.60/1Mvia AIHubMix | — | $0.40/1M | $1.60/1M | — | — | |
| Chat Latest (GPT-5.5 Instant)OpenAIPrompt cachingVision | $30/1Mvia ZenMux | 400K | $5/1M | $30/1M | 1419 | — | |
| Claude 3 Sonnet 20240229AnthropicVision | $15/1Mvia Amazon Bedrock | — | $3/1M | $15/1M | — | — | |
| Claude 4.5 SonnetAnthropicPrompt cachingReasoning+2 | $15/1Mvia Replicate | 200K | $3/1M | $15/1M | — | — | |
| Claude Haiku 4.5 (latest)AnthropicPrompt cachingJSON+3 | $4/1Mvia APIMart | 200K | $1/1M | $5/1M | — | — | |
| Claude Opus 4AnthropicPrompt cachingJSON+3 | $75/1Mvia Atlas Cloud | 200K | $15/1M | $75/1M | 1377 | — | |
| Claude Opus 4.1AnthropicPrompt cachingReasoning+2 | $75/1Mvia AI/ML API | 200K | $15/1M | $75/1M | — | — | |
| Claude Opus 4.5 (latest)AnthropicPrompt cachingJSON+3 | $20/1Mvia APIMart | 200K | $5/1M | $25/1M | — | — | |
| Claude Opus 4.6AnthropicPrompt cachingJSON+3 | $20/1Mvia APIMart | 1M | $5/1M | $25/1M | 1546 | — |
Frequently asked questions
A language model that accepts images alongside text. It can describe a picture, read the text in it, interpret charts or answer questions about a screenshot, and replies in text.
Mostly. Multimodal is the broader term: some models also take audio or video. Every model on this page reads images; each model's page lists everything else it accepts.
Start from the task: reading long documents and charts favours models with large context windows, while quick image captions can run on a small, cheap model. Compare the lowest prices here, then test two or three on your own images.
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