Collect Video Testimonials That Build TrustBest AI Coding Models
By Venkatraman Chandrasekaran
Last updated on Oct 6, 2026AI coding models are artificial intelligence models optimized for understanding, generating and reasoning about software code.
- Deep reasoning across complex, long horizon workflows
- Up to 1 million output tokens for extended reasoning trajectories
- Coding, multimodal understanding, computer use and cybersecurity defense capabilities
Long Horizon Reasoning
Gemini 4 Argon is designed for sustained reasoning across long, multi-step tasks and supports an output limit of up to 1 million tokens. Google reports a GraphWalks score of 99.7% for tasks up to 128K and 84.2% for the 256K to 1M evaluation range, providing documented evidence of performance on long context reasoning tasks.
Coding and Enterprise Workflows
Google reports a 77.9% score on DeepSWE v1.1 for long horizon software engineering and 91.9% on Vibe Code Bench. For enterprise knowledge work, Argon scored 68.9% on the Vals Index, 65.4% on Vals Finance Agent v2, 19.6% on Harvey's Legal Agent Benchmark and 51.3% on AutomationBench.
Multimodal and Computer Use Evaluation
Google documents Gemini 4 Argon performing professional chart analysis, long video understanding and reasoning across document sequences. The model scored 71.6% on Chartography and 91.7% on LVBench for multimodal understanding. For computer use, Google reports a 39.5% pass rate on Agent's Last Exam and a 69.2% partial score on the offline subset of OSWorld 2.0.
- Native multimodal understanding of images, video, audio and text
- 1 million token context window with up to 128,000 output tokens
- Deep thinking, tool calling, web search and computer use capabilities
Multimodal and Long Context Processing
MiMo-V2.6-Pro combines native understanding of text, images, video and audio with a 1 million token context window and maximum output of 128,000 tokens. Xiaomi also documents multimodal perception in computer use and embodied scenarios, where visual information can contribute to continued reasoning and actions. Its documented output modality is text.
Reasoning and Agent Capabilities
The model supports deep thinking, tool calling, web search, streaming and context caching. Xiaomi trained the V2.6 series using large scale reinforcement learning across code, general, visual and cybersecurity tasks. The release also demonstrates multi-agent workflows, software engineering, computer use and long horizon tasks where the model iterates using environmental or visual feedback.
Documented Evaluation Results
Xiaomi reports a score of 46 on the Artificial Analysis Intelligence Index for MiMo-V2.6-Pro. During reinforcement learning, its out-of-sample DeepSWE v1.1 software engineering result increased from 58.4 to 72.6. Xiaomi also reports training across approximately 750,000 cumulative trajectories for the Pro and Flash models combined during six days of live reinforcement learning.
- Text and image multimodal input with text output
- 500,000 token context window with configurable reasoning effort
- Function calling, web search, X search and code execution tools
Multimodal and Long Context Capabilities
Grok 4.7 combines text and image input with a 500,000 token context window. Image requests can include public image URLs or base64 encoded images alongside text instructions. The model produces text output and supports configurable reasoning effort at low, medium, high and xhigh levels, making both multimodal input processing and reasoning depth configurable through its developer interface.
Tool Use and Agentic Workflows
Grok 4.7 supports function calling alongside xAI hosted tools for web search, X search and code execution. These capabilities allow applications to retrieve current information, search content on X, execute code and invoke custom functions during model workflows. Context compaction is also documented for long agent loops where accumulated tool interactions can consume substantial context.
Documented Evaluation Results
xAI reports Grok 4.7 scores of 46.3% on CursorBench 4.0, 71.0% on DeepSWE v1.1 at high reasoning effort, 64.0% on EEBench, 37.6% on Terminal Bench 4.0 and 19.6% on the Harvey Legal Agent Benchmark. xAI also reports a score of 56.7% on HealthBench Professional.
- Advanced coding and complex software engineering capabilities with configurable reasoning effort.
- Extended agentic task execution involving multiple development steps, tool interaction, experimentation and verification.
- Cybersecurity research capabilities covering vulnerability discovery and security analysis.
Coding and Software Engineering Capabilities
GLM-5.3 focuses on complex software engineering and extended development workflows. Z.ai reports scores of 28.3 on Terminal-Bench 3.0 and 66.9 on DeepSWE v1.1. Its internal Z.ai Code Bench evaluates coding agents in development environments using task completion and checklist accuracy. At maximum reasoning effort, GLM-5.3 achieves a reported task completion rate of 34.5%, using approximately 75,000 output tokens per evaluated task.
Reasoning and Agentic Execution
GLM-5.3 provides three reasoning effort levels: low, high and max. Its post training incorporates reinforcement learning environments representing practical engineering and research tasks with multiple dependent steps. Z.ai reports a score of 73.0 on Toolathlon Verified and 28.5 on Agents' Last Exam. These evaluations provide additional information about the model's tool interaction and extended task execution capabilities.
Cybersecurity and API Considerations
GLM-5.3 demonstrates cybersecurity capabilities through vulnerability discovery and security research evaluations. Z.ai reports results on CyberGym and ExploitGym, using coding agent environments to evaluate security related tasks. For developers integrating the model, an important technical consideration is its mandatory thinking mode. Applications that previously disabled thinking must update their API configurations before migrating to GLM-5.3.
- Ultra-fast 1000+ tokens/sec inference on Cerebras WSE-3 hardware
- Real-time steerability with mid task interruption and redirection
- 128k context window with 80% reduced roundtrip overhead via WebSocket architecture
Infrastructure Architecture
GPT-5.3-Codex-Spark represents OpenAI's first production deployment on non GPU inference infrastructure, utilizing Cerebras' Wafer Scale Engine 3. This 4 trillion transistor processor eliminates the memory bandwidth bottlenecks inherent in discrete GPU architectures, enabling the 1000+ tokens/second throughput. The WebSocket based persistent connection architecture reduces roundtrip overhead by 80%, fundamentally changing the latency profile for interactive development tools.
Workflow Differentiation
Unlike GPT-5.3-Codex, which optimizes for autonomous execution over extended durations, Spark is explicitly tuned for collaborative iteration. The model's "lightweight" editing philosophy, minimal targeted changes without automatic test execution, prioritizes responsiveness over comprehensiveness. This creates a distinct use case: Spark excels at exploration and rapid prototyping where developer direction changes frequently, while standard Codex handles substantial refactoring requiring sustained autonomous operation.
Performance Benchmarking
On SWE-Bench Pro and Terminal Bench 2.0, Spark demonstrates that reduced latency need not sacrifice capability. The model completes software engineering tasks in a fraction of GPT-5.3-Codex's time while maintaining competitive accuracy metrics. This performance profile makes Spark particularly effective as a sub-agent in multi agent workflows, handling read heavy exploration and summarization tasks that feed into main agents running deeper reasoning models.