Claude Opus 4.6 vs Gemini 3.1 Pro vs Codex 5.3: The Definitive ComparisonFebruary 2026

This comparison draws exclusively from verified sources: official developer announcements, published system cards, and reputable third-party evaluations. We do not speculate on unreported metrics or extrapolate from older model versions. Where benchmark results conflict between sources, we note the discrepancy and cite both. All pricing is as published at the time of writing (25 February 2026). Performance claims are based on the specific benchmark versions cited — SWE-Bench Verified, Terminal-Bench 2.0, ARC-AGI-2, GPQA Diamond, and MMLU — not on internal or proprietary evaluations.

We evaluate across nine dimensions: reasoning and instruction following, coding capability, tool use and agentic workflows, multimodal support, context length and memory, safety and policy constraints, latency and cost, ecosystem and integrations, and privacy and compliance. Each dimension uses the most relevant publicly available benchmark alongside qualitative assessment from real-world usage.

Released on 5 February 2026 by Anthropic, Claude Opus 4.6 (model ID: claude-opus-4-6) represents a significant leap in reasoning depth and agentic capability. It is Anthropic's first model to receive ASL-3 safety certification — their highest classification — and comes with a 213-page system card documenting extensive safety evaluations conducted by both the UK AI Safety Institute and the US AI Safety Institute.

Key Strengths

• SWE-Bench Verified leader (80.84%) — the highest reported score on this benchmark as of publication, demonstrating exceptional real-world software engineering capability across bug fixing, feature implementation, and code review.
• Adaptive thinking — unlike manual "thinking budget" approaches, Opus 4.6 automatically scales its internal reasoning depth based on task complexity. Simple queries receive fast responses; complex architectural problems receive deep multi-step analysis.
• 128K output tokens — enabling generation of entire modules, long-form documentation, and multi-file code changes in a single response without truncation.
• 1M context window (beta) — while the standard context is 200K tokens, a 1M token beta is available to select API partners, enabling analysis of entire codebases in a single pass.
• Agent Teams & Claude Code — Anthropic's official CLI tool (Claude Code) supports multi-agent team workflows where specialised sub-agents collaborate on complex tasks. This is unique among frontier models.

Limitations

• Premium pricing — at $5.00/$25.00 per MTok (input/output), it is 2.5x more expensive than Gemini 3.1 Pro on input and roughly 2x on output. Prompt caching ($0.50/MTok) helps offset this for repeated contexts.
• No audio/video support — limited to text and image inputs, unlike Gemini's full multimodal stack.
• Terminal-Bench 2.0 gap (65.4%) — notably behind Codex 5.3 (77.3%) on interactive terminal operations, suggesting less strength in rapid command-line iteration workflows.

Pricing

Standard API: $5.00 input / $25.00 output per million tokens. With prompt caching enabled, input drops to $0.50/MTok for cached content. The Anthropic API supports batching with a 50% discount on output tokens for non-time-sensitive workloads. Enterprise plans with custom pricing are available through Anthropic's sales team.

Released on 19 February 2026, Gemini 3.1 Pro is Google DeepMind's latest frontier model and the most recent of the three in this comparison. It builds on the Gemini 2.5 architecture with native 1,000,000-token context, full multimodal support (text, image, audio, and video), and configurable thinking levels that let developers trade latency for reasoning depth.

Key Strengths

• 1,000,000-token native context — the largest production context window of any frontier model, enabling analysis of entire repositories, long documents, and multi-hour video transcripts in a single prompt.
• GPQA Diamond leader (94.3%) — the top score on graduate-level science questions, making it the strongest model for research and scientific reasoning tasks.
• ARC-AGI-2 leader (77.1%) — significantly ahead of both Claude (68.8%) and Codex (52.9%) on novel reasoning and abstraction tasks, indicating superior generalisation capability.
• Full multimodal stack — processes text, images, audio, and video natively. The only model in this comparison with audio and video understanding, making it ideal for content analysis and media-rich workflows.
• Aggressive pricing ($2/$12 per MTok) — the most cost-effective frontier model in this comparison, making it accessible for high-volume production use cases without budget constraints.

Limitations

• 64K max output — half the output capacity of Claude Opus 4.6 and Codex 5.3, which may limit single-pass generation of very large code changes or documents.
• Agentic ecosystem maturity — while Google offers Vertex AI Agent Builder, the agentic tooling ecosystem is less mature than Anthropic's Claude Code or OpenAI's Codex CLI for terminal-native workflows.
• Data residency concerns — Google's infrastructure spans many regions but some enterprises, particularly in regulated industries, may have concerns about data processing within Google's ecosystem.

Pricing

Standard API via Google AI Studio and Vertex AI: $2.00 input / $12.00 output per million tokens. A free tier is available through Google AI Studio with rate limits. Context caching further reduces costs for repeated prompts. Vertex AI offers enterprise SLAs, private endpoints, and CMEK encryption.

Released on 5 February 2026, Codex 5.3 — officially named GPT-5.3-Codex — is OpenAI's purpose-built coding model. Built on the GPT-5 architecture, it focuses on interactive agentic coding with particular emphasis on terminal operations. On 9 February 2026, GitHub announced that Codex 5.3 powers the generally available GitHub Copilot agent, bringing agentic coding to millions of developers.

Key Strengths

• Terminal-Bench 2.0 dominance (77.3%) — the highest score by a significant margin, demonstrating exceptional capability in interactive terminal operations, shell scripting, and command-line debugging workflows.
• SWE-Bench Pro leader (56.8%) — ahead of Gemini 3.1 Pro (54.2%) on the harder professional-grade subset, indicating strong performance on complex, multi-step software engineering tasks.
• Spark variant (1,000+ tok/s) — a Cerebras-powered variant that achieves over 1,000 tokens per second output speed, enabling near-real-time code generation for pair programming and rapid prototyping.
• GitHub Copilot GA — deep integration with the world's largest developer platform. The Copilot agent powered by Codex 5.3 can create branches, write code, run tests, and open pull requests autonomously.
• 400K context window — double Claude's standard context, sufficient for analysing large codebases while being more readily available than Claude's gated 1M beta.

Limitations

• ARC-AGI-2 weakness (52.9%) — significantly behind both Claude (68.8%) and Gemini (77.1%) on novel reasoning tasks, suggesting less generalisation beyond code-specific domains.
• Unconfirmed pricing — as of February 2026, OpenAI has not publicly disclosed per-token pricing for the Codex 5.3 API, making cost comparison difficult for enterprise procurement.
• Code-focused scope — primarily optimised for text and code, lacking the multimodal breadth of Gemini or the deep reasoning versatility of Claude for non-coding enterprise tasks.

Pricing

Codex 5.3 is available through the OpenAI API (model IDs in the GPT-5 family) and via GitHub Copilot subscriptions. Per-token API pricing has not been publicly confirmed as of 25 February 2026. GitHub Copilot Individual costs $10/month, while Copilot Enterprise (which includes the Codex agent) costs $39/user/month.