The best agentic coding model available today can spin up a development environment, write and debug a full application, push to a git repository, and configure a deployment pipeline — without a human touching the keyboard. That shift from code autocomplete to autonomous software engineer has happened faster than most predicted, and the gap between leading models is now measured in the complexity of tasks they can complete unassisted, not just the quality of code they generate in a single prompt.
Modern agentic coding models operate through live terminal sessions, calling shell commands, reading error logs, installing dependencies, and iterating on failures the same way a developer would. The best ones can hold a task in context for hours — sometimes more than half a working day — navigating ambiguity, recovering from mistakes, and producing working software at the end. The worst ones get stuck in loops, hallucinate package names, or lose the thread of what they were supposed to build.
That’s exactly what Artificial Analysis’s Terminal-Bench Hard benchmark is designed to expose. Rather than testing code generation in isolation, it drops models into real terminal environments and scores them on task completion: compiling software, configuring servers, running data pipelines, debugging build failures. The result is the most operationally honest ranking of agentic coding performance available today — and the top ten models on that list are what this article covers.
1. GPT-5.4 xhigh
Terminal-Bench Hard Score: 58%
OpenAI’s GPT-5.4, released on March 5, 2026, sits at the top of the Terminal-Bench Hard leaderboard and represents the current apex of what any best agentic coding model can do in a live terminal. GPT-5.4 consolidates what were previously separate model families — combining the frontier agentic coding strengths of GPT-5.3-Codex with improved general reasoning and native computer-use capabilities in a single unified architecture. The Pro variant uses extended parallel test-time compute to push performance even further on the most demanding multi-step tasks. On OSWorld-Verified, it achieves a 75% success rate — surpassing human benchmarks — and supports a 1 million token context window in the API. OpenAI describes GPT-5.4 as its first general-purpose model with native computer-use capabilities, enabling agents to interact directly with software through screenshots, mouse commands, and keyboard inputs. On GDPval, which tests agents across 44 occupations spanning major industries, GPT-5.4 matches or exceeds industry professionals in 83% of comparisons. For organizations running the most demanding autonomous software engineering pipelines, this is the model to beat.
2. Gemini 3.1 Pro Preview
Terminal-Bench Hard Score: 54%
Google DeepMind’s Gemini 3.1 Pro, released on February 19, 2026, is the highest-performing non-OpenAI model on the Terminal-Bench Hard index and a genuinely formidable best agentic coding model contender. Built on a Transformer-based Mixture-of-Experts architecture on top of Gemini 3 Pro, it represents a point-version update within the Gemini 3 series specifically targeting complex problem-solving requirements, long-horizon agentic workflows, and native multimodal code generation. The model introduces a 1 million token context window with an expanded output limit of 65,536 tokens — resolving the truncation limitations that hampered earlier models in long coding sessions — and achieves an 80.6% pass rate on SWE-Bench Verified. A distinctive architectural feature is a new three-tier thinking system that lets developers modulate compute time between low, medium, and high modes, offering a tunable balance between latency and reasoning depth. On Gemini 3.1 Pro, improved tool use and simultaneous multi-step task execution make it particularly well-suited to the kind of agentic coordination that terminal-heavy workflows demand.
3. GPT-5.3 Codex
Terminal-Bench Hard Score: 53%
OpenAI’s GPT-5.3-Codex, released February 5, 2026, is purpose-built for software engineering agents and is arguably the single most specialized best agentic coding model in OpenAI’s lineup. It advances the frontier coding performance of its predecessor GPT-5.2-Codex while incorporating the general reasoning improvements of GPT-5.2, resulting in a model that OpenAI says was itself instrumental in its own development — the Codex team used early versions to debug training, manage deployment, and diagnose evaluations. GPT-5.3-Codex sets state-of-the-art performance on both SWE-Bench Pro and Terminal-Bench 2.0, achieving results with fewer tokens than any prior model, which translates to lower real-world costs on long agentic sessions. With 25% faster execution than GPT-5.2-Codex and 2–4x better token efficiency per task, it excels at code review, identifying edge cases, and rapid iteration cycles. One independent developer analysis put its Terminal-Bench 2.0 score at 77.3%, making it the clear leader for terminal-based execution speed and continuous integration pipelines.
4. Claude Opus 4.6
Terminal-Bench Hard Score: 53%
Anthropic’s Claude Opus 4.6, released February 5, 2026, matches GPT-5.3-Codex on Terminal-Bench Hard and has established itself as the best agentic coding model for teams running the most complex, long-horizon software engineering workflows. It introduced the first 1M token context window in the Opus lineage, and as of February 20, 2026, holds the longest task-completion time horizon of any model evaluated by METR — with a 50%-success time horizon of 14 hours and 30 minutes, meaning it can work independently through complex tasks for well over half a working day without losing coherence. In a notable demonstration, 16 Claude Opus 4.6 agents working in parallel were able to write a C compiler in Rust from scratch capable of compiling the Linux kernel — the first time any model has achieved this. On Terminal-Bench 2.0, it scored 65.4%, and on SWE-Bench Verified, it reached 80.8%. Claude Opus 4.6 was priced at $5/$25 per million input/output tokens at launch, with a premium tier for long-context requests exceeding 200K tokens.
5. Claude Sonnet 4.6
Terminal-Bench Hard Score: 49%
Anthropic released Claude Sonnet 4.6 just twelve days after Opus 4.6, on February 17, 2026, and it immediately became the default model across claude.ai’s free and pro plans. For most developers, Claude Sonnet 4.6 is the most practically compelling best agentic coding model available today: it delivers 98% of Opus 4.6’s performance at 60% of the cost ($3/$15 per million input/output tokens), with substantially lower latency. On SWE-Bench Verified, it scores 79.6% and achieves 72.5% on OSWorld-Verified — matching frontier-level performance in computer use, including navigating browsers, spreadsheets, and local file systems. The model features Adaptive Thinking, a dynamic reasoning engine that scales compute based on task complexity without requiring the developer to manually toggle reasoning modes. A 1M token context window in beta and automated context compaction that summarizes older conversation turns make it particularly strong for continuous, long-running agentic coding loops. Enterprise customers, including GitHub, have described Sonnet 4.6 as their go-to for deep codebase work that would previously have required more expensive models.
6. GPT-5.2
Terminal-Bench Hard Score: 47%
OpenAI’s GPT-5.2, released December 11, 2025, is the general-purpose frontier model that preceded the more specialized Codex variants and remains a strong best agentic coding model for professional knowledge work. It was released under an internal “code red” directive from CEO Sam Altman, who accelerated the launch in direct response to Gemini 3 topping AI benchmarks — and GPT-5.2 delivered a decisive response, outperforming competitors on GDPval, SWE-Bench Pro, and GPQA Diamond. The model comes in three variants — Instant, Thinking, and Pro — with the Thinking variant scoring 55.6% on SWE-Bench Pro and achieving a 50%-time horizon of 6 hours and 34 minutes for autonomous task completion. Its 400,000-token context window and August 2025 knowledge cutoff support large-scale professional workflows. Multiple enterprise customers, including Box and Databricks, reported measurable gains in real-world knowledge work and agentic data science tasks at launch, and the model was priced at $1.75 per million input tokens — below competing frontier models despite its capabilities.
7. Claude Opus 4.6 (Max)
Terminal-Bench Hard Score: 46%
The extended “max” configuration of Claude Opus 4.6 represents Anthropic’s highest-effort reasoning profile for the same underlying model, with the effort parameters pushed to their maximum settings. As a best agentic coding model configuration rather than a separate release, Opus 4.6 Max applies the model’s deepest available thinking budget to each task — a setup that Anthropic itself uses for the most demanding agentic evaluations. The slight performance gap compared to the base Opus 4.6 entry in the Terminal-Bench Hard rankings reflects the tradeoffs in evaluation conditions rather than a fundamental capability difference; Opus 4.6 Max is effectively the model at its ceiling performance, which manifests as slower throughput in exchange for maximum accuracy on complex multi-step terminal tasks. For teams where a failed agent run carries significant cost — such as production deployment automation or large codebase refactors — the additional inference time of the max configuration is often justified.
8. GLM-5
Terminal-Bench Hard Score: 43%
GLM-5, developed by Zhipu AI and Tsinghua University, is the highest-ranked Chinese lab model on the Terminal-Bench Hard benchmark and represents the most competitive best agentic coding model to emerge from China’s rapidly accelerating AI research ecosystem. The GLM (General Language Model) series has been a flagship project from Tsinghua’s KEG Lab, and GLM-5 marks a significant generational leap over its predecessors in agentic tool use and multi-step task completion. Zhipu AI has positioned GLM-5 competitively against international frontier models on coding benchmarks and deployed it commercially through the Zhipu AI open platform, making it accessible to Chinese enterprises and international developers via API. The model’s presence in the global top 10 on a rigorous agentic terminal benchmark reflects the growing maturity of Chinese AI labs, which have moved beyond matching proprietary models in conversational tasks to competing directly in the more demanding domain of autonomous software engineering.
9. Qwen 3.5
Terminal-Bench Hard Score: 41%
Qwen3.5 is Alibaba’s most powerful model and the best agentic coding model to emerge from the broader Qwen3 family, which has collectively surpassed 20 million downloads globally. Officially launched at Apsara Conference 2025 in September, Qwen3-Max crosses the 1 trillion parameter mark using a sparse Mixture-of-Experts architecture that was pretrained on approximately 36 trillion tokens with an emphasis on multilingual, coding, and STEM data. On SWE-Bench Verified, its Instruct mode scores 69.6%, and on Tau2-Bench — a measure of conversational agent tool use — it reached 74.8, outperforming Claude Opus 4 and DeepSeek V3.1 at the time of its release. The model ships in two runtime tracks: Instruct for lower-latency coding and reasoning tasks, and Thinking for longer deliberation and explicit tool calls suited to agent workflows. Its APIs are designed to be OpenAI-compatible, reducing integration friction for teams migrating from other providers.
10. Gemini 3 Flash
Terminal-Bench Hard Score: 39%
Rounding out the top ten, Google DeepMind’s Gemini 3 Flash is the fastest and most cost-efficient best agentic coding model in the Gemini 3 family, and its presence in the global Terminal-Bench Hard top 10 underscores how far efficiency-oriented models have come. Gemini 3 Flash was designed for near-real-time performance while retaining the multimodal capabilities and agentic tool use that characterize the broader Gemini 3 generation — launched alongside Gemini 3 Pro in November 2025. It has ranked among the top models on LMArena’s Text Arena and Vision leaderboards, making it a popular daily driver for developers who need a capable best agentic coding model without the inference cost of frontier Pro models. On tasks requiring strategic reasoning with live inputs — such as video game guidance using hand-tracking or real-time UI generation — Gemini 3 Flash delivers near-real-time responses that more powerful models cannot match at scale. For teams building interactive coding tools, CI/CD pipelines, or high-throughput agent systems, Flash occupies a unique niche: frontier-adjacent intelligence at production-friendly economics.
How to Read These Rankings
Terminal-Bench Hard is one of the most signal-rich benchmarks available for evaluating agentic coding models because it tests actual execution in real terminal environments — not question answering or static code generation. A model that scores well here can compile software, configure servers, run data pipelines, and debug failures through a shell prompt without human intervention.
That said, no single benchmark captures everything. The best agentic coding model for your team depends on what you’re building. For long-horizon codebase work, Claude Opus 4.6’s 14-hour autonomous task horizon and 1M context window are hard to beat. For terminal execution speed and CI/CD integration, GPT-5.3-Codex’s token efficiency wins on economics. For teams that need a generalist model that can also handle knowledge work, GPT-5.4 and Gemini 3.1 Pro are both exceptional.
The speed of model releases shows no signs of slowing. This ranking reflects the state of play as of March 2026 — expect significant changes within months.