Top Chart Question Answering Models

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Claude Fable 5 is Anthropic's first generally available Mythos-class large language model, released on June 9, 2026. It is built for long-horizon, asynchronous, and agentic tasks that prior Claude generations could not sustain, including multi-day autonomous coding sessions, complex knowledge work, and document-heavy analysis. The model supports a 1 million token context window with up to 128,000 output tokens per request and uses adaptive thinking as its sole reasoning mode, where the effort level is adjustable but raw chain-of-thought is never returned. Vision capabilities allow the model to parse diagrams, charts, and tables embedded in files and PDFs, and to use visual feedback to evaluate its own coding outputs against design goals. On benchmarks such as SWE-Bench Pro, the model scores 80.3% compared to 69.2% for Claude Opus 4.8, and it leads on CursorBench 3.1 for autonomous coding workflows.Claude Fable 5 shares the same underlying model weights as Claude Mythos 5, but is deployed with safety classifiers that automatically reroute queries in high-risk domains — including cybersecurity, biology, and chemistry — to Claude Opus 4.8. These classifiers trigger in fewer than 5% of sessions on average. As a designated Covered Model, all traffic is subject to mandatory 30-day data retention to support safety monitoring. The model is available via the Claude API, Amazon Bedrock, Vertex AI, and Microsoft Foundry. Anthropic has not publicly disclosed parameter count, architecture details, or training data composition for this model.

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

Qwen3.6-27B is a dense 27-billion-parameter multimodal language model developed by Alibaba's Qwen team and released on April 22, 2026. It combines a causal language model with an integrated vision encoder, supporting text, image, and video inputs natively. The architecture employs a hybrid attention design that interleaves Gated DeltaNet linear attention blocks with standard Gated Attention layers across 64 transformer layers with a hidden dimension of 5,120. Unlike Mixture-of-Experts variants in the Qwen3.6 family, all 27 billion parameters are active on every inference pass, simplifying deployment and quantization. The model supports a native context window of 262,144 tokens, extensible to approximately 1,010,000 tokens via YaRN scaling. It is released under the Apache 2.0 license with open weights available on Hugging Face and ModelScope.The model introduces two notable capabilities relative to prior Qwen releases: enhanced agentic coding support covering frontend workflows and repository-level reasoning, and a Thinking Preservation mechanism that retains chain-of-thought reasoning context across multi-turn conversation history to reduce redundant token generation in iterative agent sessions. It supports both a thinking mode for multi-step reasoning and a non-thinking mode for faster responses within a single model. On coding benchmarks, Qwen reports scores of 77.2 on SWE-bench Verified, 59.3 on Terminal-Bench 2.0, and 48.2 on SkillsBench. Vision capabilities include chart understanding (CharXiv RQ: 78.4), OCR (CC-OCR: 81.2), and video understanding (VideoMME with subtitles: 87.7).

Qwen3.6-Flash is the production API variant of the Qwen3.6 model series, developed by the Qwen team at Alibaba Group. It is built on the Qwen3.6-35B-A3B architecture, which combines a hybrid linear attention mechanism with sparse Mixture-of-Experts (MoE) routing to achieve high-throughput inference with reduced latency. The model is natively multimodal, processing both text and images within a unified early-fusion architecture, and supports 201 languages and dialects. It operates in a hybrid thinking mode, capable of generating explicit chain-of-thought reasoning before producing a final response, with the option to disable thinking for direct output. A Thinking Preservation feature allows reasoning context to be retained across multi-turn conversations, which is particularly useful for iterative agentic workflows.The model is trained with reinforcement learning scaled across large-scale agent environments and covers a broad range of tasks including agentic coding, frontend development, visual understanding, document processing, and tool use. Compared to the open-weight Qwen3.6-35B-A3B, the Flash API variant extends the default context window to 1 million tokens and includes built-in production features such as native function calling and official tool integrations. The underlying architecture achieves near-100% multimodal training efficiency relative to text-only training, and the model demonstrates strong performance on agentic coding benchmarks including SWE-bench Verified.

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder-decoder architecture by Zhipu AI. The model combines a 0.4B-parameter CogViT visual encoder pre-trained on large-scale image-text data, a lightweight cross-modal connector with efficient token downsampling, and a 0.5B-parameter GLM language decoder, totaling 0.9B parameters. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. Training proceeds through four stages: visual encoder pretraining with MIM, CLIP, and distillation objectives; vision-language pretraining on document parsing, grounding, and VQA data; supervised fine-tuning on curated OCR datasets covering text, formula, table, and key information extraction; and full-task reinforcement learning to improve accuracy and structural consistency.At the system level, GLM-OCR adopts a two-stage pipeline in which PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. This design enables robust handling of diverse document layouts including tables, formulas, and multi-column text. The model supports document parsing and targeted recognition tasks, producing structured outputs in Markdown, JSON, and LaTeX formats across more than 100 languages. On the OmniDocBench V1.5 benchmark, GLM-OCR scores 94.62, and achieves 94.0 on OCRBench and 96.5 on UniMERNet for formula recognition.