MULTIMODAL REASONING · 46 REVIEWED
The Best Multimodal Reasoning Models (2026)
Models that reason over images, documents, audio or video alongside text rather than describing them at arm's length. This is what makes document extraction, screenshot understanding and visual QA work, and it is where the gap between models is widest.
Ranked by hand · 34 with a free tier · updated 2026-08-04
CATEGORY SNAPSHOT
TOP 10 COMPARED
| Tool | Pricing | API | Open weights | Best for |
|---|---|---|---|---|
| Claude Fable 5 | Paid | No | No | Creative Writing |
| Claude Opus 5 | Freemium | Yes | No | Best Frontier Model Overall |
| DeepSeek | Freemium | Yes | Yes | Cost-Efficiency |
| Kimi k1.5 | Freemium | Yes | Yes | Technical Reasoning |
| Qwen 2.5-VL | Free | Yes | Yes | Open Vision Reasoning |
| Llama 3.2 Vision | Free | Yes | Yes | Open Ecosystem Support |
| Pixtral Large | Freemium | Yes | Yes | Complex Visual Reasoning |
| InternVL 2.5 | Free | Yes | Yes | Leaderboard-Topping Vision |
| Gemini 3.5 Flash | Freemium | No | No | Fast Multimodality |
| Gemini Omni | Freemium | No | No | Unified Generation |
ALL 46 MULTIMODAL MODELS
Anthropic's Mythos-class creative model
Claude Fable 5 is Anthropic's Mythos-class model released on June 9, 2026, focused on creative writing, worldbuilding, and narrative depth
Why: Claude Fable 5 is notable as Anthropic's most experimental creative model. Even with limited availability, it represents an interesting direction for AI-assisted fiction and long-form creative work.
Paid
Best for Creative Writing
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Anthropic's frontier model, currently first on the Artificial Analysis Intelligence Index
Claude Opus 5 is Anthropic's flagship model, released 24 July 2026 with a 1M-token context window and five selectable effort levels (low, medium, high, xhigh, max)
Why: It is the current number one on the independent Artificial Analysis Intelligence Index, and it got there while costing less per task than the model it displaced: $2.03 average per index task against Fable 5's $2.75. The effort dial is the reason to pick it over a fixed-tier model, because one integration covers cheap high-volume calls and expensive long-horizon agent runs.
Freemium
Best Frontier Model Overall
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The Efficiency Revolution: Frontier Intelligence at 1/100th the Cost
DeepSeek is the architect of the 'DeepSeek movement,' a fundamental shift in AI development that prioritizes extreme efficiency over raw compute
Why: DeepSeek changed the game by proving that 'expensive' doesn't always mean 'better.' We picked it because it's the first model family to offer true frontier-level reasoning (R1), general intelligence (V3), and advanced vision/OCR (VL2) with an open-weight philosophy and an API price point that makes proprietary models look obsolete.
Freemium
Best for Cost-Efficiency
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The 'Next DeepSeek' Movement: o1-Level Reasoning at 1/100th the Cost
Kimi k1
Why: Kimi k1.5 is the first model to prove that o1-level reasoning is achievable through efficient, open-weight architectures. We selected it because it consistently matches or exceeds Claude 4.5 in technical benchmarks (AIME, MATH-500) while offering a 2M context window and a significantly lower API price point, making frontier intelligence accessible to everyone.
Freemium
Best for Technical Reasoning
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The Open Vision-Reasoner: SOTA Multimodal Performance
Qwen 2
Why: We added Qwen 2.5-VL to the Open Frontier movement because it is currently the highest-performing open-weight vision model. It proves that open source can lead in multimodal reasoning, especially for tasks requiring high-resolution OCR and long-form video understanding.
Free
Best for Open Vision Reasoning
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Meta's Open Multimodal Standard
Llama 3
Why: We included Llama 3.2 Vision because it is the most widely supported open multimodal model in the world. Its integration into almost every AI tool and framework makes it the 'default' choice for open-weight vision reasoning.
Free
Best for Open Ecosystem Support
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The Open Vision Frontier: 124B Multimodal Power
Pixtral Large is Mistral AI's flagship 124B parameter multimodal model, designed to compete directly with GPT-4o and Claude 3
Why: We added Pixtral Large because it represents the peak of European open-weight AI. It is one of the few open models that truly matches the visual reasoning depth of the top proprietary models, making it essential for the Open Frontier movement.
Freemium
Best for Complex Visual Reasoning
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The Open-Source Vision Giant: 78B Multimodal Leader
InternVL 2
Why: We included InternVL 2.5 because it is a consistent leaderboard champion. It often outperforms much larger models in visual reasoning and OCR, making it a critical tool for developers who need GPT-4 level vision without the proprietary lock-in.
Free
Best for Leaderboard-Topping Vision
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Google's fast, capable multimodal model from I/O 2026
Gemini 3
Why: Gemini 3.5 Flash hits a practical sweet spot for developers and creators who need more capability than entry-level models but do not require the full cost of an Ultra model. Its native multimodal design makes it especially useful for mixed-media tasks.
Freemium
Best for Fast Multimodality
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Google's unified multimodal generation model
Gemini Omni is a single Google model announced at I/O 2026 that can generate and reason across text, images, video, and audio from unified prompts
Why: Gemini Omni represents Google's push toward a single model for all media types. For teams building multimodal products, it simplifies architecture by replacing multiple specialized endpoints with one interface.
Freemium
Best for Unified Generation
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DeepSeek's open-weight model with permanent pricing
DeepSeek V4-Pro is a high-performance language model from DeepSeek
Why: DeepSeek V4-Pro stands out for combining frontier-level performance with transparent, permanent pricing and open weights. It is a practical choice for teams that want to self-host or avoid unpredictable API costs.
Freemium
Best for Predictable Pricing
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StepFun's 198B MoE vision-language model
StepFun Step 3
Why: Step 3.7 Flash offers a competitive Chinese-frontier multimodal model with an MoE architecture that balances capability and inference cost. It is a useful option for vision-language applications and for teams exploring alternatives to US models.
Freemium
Best for Efficient VLM
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The frontier model for complex reasoning and software architecture
OpenAI's most advanced model to date, featuring a 2M context window and specialized training for complex multi-step reasoning
Why: GPT-5.3 Codex is the 'World's Smartest Planner.' While other AI tools are good at chatting, this one is built for solving huge, difficult problems like planning how a whole software system should work. It has a massive memory (2 million words) so it never loses track of the big picture.
Paid
Best for Reasoning
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Native multimodal intelligence with a 10M context window
Google's most powerful multimodal model, capable of processing hours of video, thousands of lines of code, or massive document sets in a single prompt
Why: Gemini 3 Ultra offers an unbeatable 10M token context window, allowing it to process entire project histories, hours of video, or massive codebases in a single prompt. Its native multimodal intelligence makes it the only model capable of 'seeing' and 'hearing' complex data sets with the same level of depth as it reads text, providing a unique advantage for large-scale data analysis.
Paid
Best for Context
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One multimodal model for text, vision, audio, and video reasoning
Nemotron 3 Nano Omni is NVIDIA's compact-but-capable multimodal stack for agentic workflows: one family of endpoints that accept text, images, audio, or video (depending on route) and return text answ...
Why: If your product roadmap says 'agents that see and hear the world,' Omni is built for that integration story, fewer moving parts than bolting Whisper + CLIP + LLM together by hand.
Paid
Best for Agents
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Alibaba's strongest vision-language model
Qwen-VL-Max is a high-performance vision-language model from Alibaba, capable of understanding images, charts, and documents, and answering questions about them
Freemium
Best for Vision-Language
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Frontier Opus model with higher-resolution vision and xhigh effort
Anthropic's frontier Opus-tier model announced on April 16, 2026, with substantial gains on the hardest coding tasks, higher-resolution vision input, a new xhigh effort level, file-system-based memory...
Why: Opus 4.7 introduced the xhigh effort level and file-system memory recall, making it a notable step between Opus 4.6 and Opus 4.8.
Enterprise
Best for Hard Coding Tasks
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Limited-availability Mythos-class model without Fable 5 safety classifiers
Anthropic's Mythos-class model announced on June 9, 2026, shares the same capabilities as Claude Fable 5 without the safety classifiers
Why: Mythos 5 is a notable limited-availability variant of the Mythos-class tier, distinct from the generally available Fable 5.
Enterprise
Best for Controlled Research
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High-volume DeepSeek inference with a 1M-token context window
DeepSeek V4-Flash is the efficient sibling of V4-Pro, offering a 1M-token context window and configurable thinking modes at a fraction of the API cost
Why: V4-Flash delivers the same 1M context and thinking modes as V4-Pro at roughly one-third the API cost, making it the practical default for most production workloads.
Freemium
Best for High-Volume APIs
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The open-weight reasoning model that sparked the efficiency revolution
DeepSeek R1 is a 671B-parameter open-weight reasoning model that matches o1-class performance on math, code, and logic benchmarks through reinforcement learning on verifiable tasks
Why: R1 proved that open-weight models can match proprietary reasoning systems at a fraction of the cost, making it a landmark for reproducible AI research.
Freemium
Best for Open Reasoning
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The 128K-context MoE flagship that introduced sparse attention
DeepSeek V3
Why: V3.2 introduced DeepSeek Sparse Attention and unified thinking modes, making it the architectural bridge that enabled the later 1M-context V4 family.
Freemium
Best for Long-Context MoE
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Multimodal coding and visual-reasoning agent model
GLM-5V-Turbo is a vision-language variant of the GLM-5 family, built for multimodal coding, visual reasoning, and image-plus-text agent workflows
Why: GLM-5V-Turbo is the GLM family's main vision agent, letting coding and agent workflows reason over images and screenshots in the same long context.
Paid
Best for Multimodal Coding
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Vision-language model for visual reasoning and UI replication
GLM-4
Why: GLM-4.6V is the practical vision tier for turning screenshots and images into working code or structured analysis.
Paid
Best for Visual Reasoning
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Document parsing model for PDF and image OCR
GLM-OCR is a specialized GLM model for extracting structured Markdown from PDFs and images
Why: GLM-OCR fills a clear gap in the GLM family by turning scanned documents and PDFs into structured, usable text with layout awareness.
Paid
Best for Document Parsing
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Free vision model for image understanding and document snapshots
GLM-4V-Flash is a free-tier vision model in the GLM family, offering zero-cost image understanding and document snapshot analysis with a 16K context window
Why: GLM-4V-Flash is the entry-level vision option for GLM, letting users test multimodal document understanding before upgrading to paid vision tiers.
Free
Best for Free Vision
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Google's long-context multimodal flagship with up to 2M tokens
Gemini 1
Freemium
Best for Long Context
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Fast, cost-efficient multimodal model with a 1M context window
Gemini 1
Freemium
Best for Fast Multimodal Tasks
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Google's low-latency agentic model with native tool use
Gemini 2
Freemium
Best for Agentic Apps
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Google's high-performance reasoning model with advanced coding
Gemini 2
Freemium
Best for Complex Reasoning
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Google's open multimodal model for research and developers
Gemma 3 is an open-weights family of multimodal models from Google, ranging from 1B to 27B parameters
Free
Best for Open Multimodal
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xAI's long-context flagship with a 1M-token window
Grok 4
Why: Grok 4.3 is the sweet spot in xAI's lineup for anyone who needs a frontier model with a very large context window at a lower price than Grok 4.5.
Paid
Best for Long-Context Work
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xAI's 2M-context beta model with multi-agent capabilities
Grok 4
Why: Grok 4.20 remains notable as xAI's first multi-agent beta model with a 2M context window, even though newer 4.3/4.5 models now offer flagship alternatives.
Paid
Best for Multi-Agent Beta Work
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Tencent's Mamba-powered deep-thinking reasoning model
Hybrid Mamba-Transformer MoE reasoning model released March 2025, built on Hunyuan TurboS with 52 billion active parameters and a 256K context window
Why: One of the first ultra-large Mamba-Transformer MoE reasoning models, offering strong benchmark scores and a 256K context window.
Freemium
Best for Reasoning
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The deep-thinking variant of Hunyuan 2.0
Open-weight reasoning variant of Hunyuan 2
Why: Hunyuan 2.0's reasoning mode for tasks that benefit from longer thought chains.
Freemium
Best for Reasoning
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Moonshot's open-weight multimodal generalist with agent swarms
Kimi K2
Why: Kimi K2.5 was Moonshot's first widely available open-weight multimodal generalist and remains a notable reference point for the K2 family before K2.6 and K3 arrived.
Freemium
Best for Open Multimodal Agents
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Moonshot's open-weight multimodal successor with long-context coding stability
Kimi K2
Why: Kimi K2.6 improves on K2.5 with stronger long-context coding and is a practical open-weight alternative for teams that want multimodal agents without the cost of closed frontier models.
Freemium
Best for Long-Context Coding
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Meta's open-weight flagship with native multimodal reasoning
Llama 4 Maverick is Meta's flagship open-weight model, released in April 2025 as part of the Llama 4 family
Why: Maverick is the top open-weight model Meta actually ships today, with strong multimodal reasoning and a practical API ecosystem, making it the default choice for open Llama deployments.
Free
Best for Open Multimodal Reasoning
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Long-context, efficient open multimodal model for edge and single-GPU use
Llama 4 Scout is Meta's efficient Llama 4 variant, released in April 2025
Why: Scout is notable for its extreme 10M-token context window and efficient single-GPU deployment, making it the standout open model for very long documents and memory-heavy applications.
Free
Best for Long Context
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Mistral's flagship open-weight multimodal frontier model
A 675B-parameter sparse mixture-of-experts model with 41B active parameters and a 262K context window, released under Apache 2
Why: Mistral Large 3 is one of the most capable permissive open-weight models available, offering frontier performance with the deployment flexibility of Apache 2.0 licensing.
Freemium
Best for Open-Weight Frontier
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Unified open-source small model for chat, reasoning, vision, and coding
A 119B-parameter MoE model with 6B active parameters and a 256K context window, released under Apache 2
Why: Small 4 packs flagship-class reasoning, vision, and coding into a single open-source model that is efficient enough for high-throughput and local deployments.
Freemium
Best for Efficient Open Multimodal
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Layout-aware document parsing that goes beyond OCR
A general-purpose document parsing model that overcomes traditional OCR limitations by understanding complex page layouts
Why: Turns messy documents into clean structured data, improving downstream RAG and agent pipelines with layout-aware extraction.
Free
Best for Document Intelligence
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Multimodal 4B safety model for text and image moderation
A 4B-parameter multimodal, multilingual small language model designed as a robust content-safety moderator
Why: A compact, open safety model that can enforce both standard and custom content policies with reasoning traces for safer deployments.
Free
Best for Content Safety
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Open foundation model for humanoid robot reasoning and control
NVIDIA's open foundation model for humanoid robot reasoning and control, combining an Eagle-based vision-language backbone with a diffusion transformer (DiT) action head for language-conditioned manip...
Why: NVIDIA's open contribution to humanoid robot foundation models, enabling language-conditioned manipulation across diverse robot embodiments.
Free
Best for Humanoid Robotics
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Microsoft's unified AI model family from Build 2026
Microsoft announced a family of MAI-branded models at Build 2026, including MAI-Thinking-1 for reasoning, MAI-Image-2
Why: The MAI family gives Microsoft a cohesive, enterprise-ready AI stack. For organizations already using Microsoft services, these models reduce friction by running inside familiar tools rather than requiring separate platforms.
Enterprise
Best for Microsoft Ecosystem
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Meta's closed-weight agentic model, and its first paid model API
Muse Spark 1
Why: This is the release where Meta stopped giving models away. Muse Spark is closed, metered and sold through Meta's own API, and it lands at rank 15 on the independent index while undercutting comparable models on price. Worth tracking for that reason alone if your stack assumed Meta meant open weights.
Freemium
Best Value for Agentic Multimodal Work
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Thinking Machines' 975B Apache-2.0 model that takes text, images and audio natively
Inkling is the first open-weights model from Thinking Machines Lab, released 15 July 2026 under Apache 2
Why: It is the first roughly trillion-parameter open-weights model that takes audio and images natively rather than through a bolted-on encoder, and Apache 2.0 means the weights can be used commercially without asking anyone. Thinking Machines is candid that this is not the strongest model available but a base worth customising, which is a more honest pitch than most open releases make.
Free
Best Open-Weight Multimodal Base
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HOW TO CHOOSE
What actually decides between multimodal models:
- Which modalities, and in which direction: Reading an image is not the same capability as generating one. Confirm the model accepts what you have and returns what you need.
- Document and chart accuracy: The common real job is pulling structure out of a PDF, a table or a chart. Benchmark scores on general vision tasks barely predict this — test on your own documents.
- Resolution and page limits: Many models downsample images before processing, which is exactly where small text is lost. Check the effective resolution, not the accepted file size.
- Grounding and citation: For extraction work, a model that points at where in the document it found something is worth more than one that is slightly more accurate and cannot show you.
- Cost per image or page: Images consume tokens at a very different rate from text. Price a realistic batch before assuming the text pricing applies.