Overview
Three AI giants. One major question: which should power your work in 2026?
On February 5, 2026, Anthropic dropped Claude Opus 4.6 — and OpenAI responded twenty minutes later with GPT-5.3 Codex. Google's Gemini 3 Pro, already established as a multimodal powerhouse, rounds out this frontier trio. All three now offer advanced reasoning and "thinking" modes that let models pause and reason through hard problems before responding.
This comparison covers benchmark performance, pricing, real-world coding results, reasoning capabilities, and which model wins for specific workflows — so you can make an informed choice rather than guessing from marketing copy.
As of February 19, 2026, the honest answer is: no single model dominates everything. Each leads in different areas, and knowing where each excels is the key to getting the most from your AI investment.
Model Snapshot: At a Glance
| Feature | Claude Opus 4.6 | GPT-5.3 Codex | Gemini 3 Pro |
|---|---|---|---|
| Released | Feb 5, 2026 | Feb 5, 2026 | Nov 2025 |
| Context Window | 1M tokens (beta) | 400K tokens | 2M tokens |
| Max Output | 128K tokens | 128K tokens | 64K tokens |
| Thinking Mode | Adaptive Thinking (4 levels) | High-compute reasoning | Deep Think mode |
| Input Pricing | $5/M tokens ($7.50 over 200K) | TBD (API pending) | $2/M tokens |
| Output Pricing | $25/M tokens ($37.50 over 200K) | TBD | $12/M tokens |
| Best For | Coding, agentic workflows, enterprise | Speed, terminal tasks, computer use | Multimodal, cost efficiency, long docs |
Benchmark Performance: The Numbers
Benchmarks are imperfect but useful. Here is how the three models compare on the tests that matter most as of February 2026.
Coding Benchmarks
| Benchmark | Claude Opus 4.6 | GPT-5.3 Codex | Gemini 3 Pro |
|---|---|---|---|
| SWE-bench Verified | 80.8% | 78.2%* | ~74.2% |
| Terminal-Bench 2.0 | 65.4% | 77.3% | ~54% |
| OSWorld (computer use) | 72.7% | Higher (leads) | — |
| Sonar Pass Rate | 83.62% | 80.66% | 81.72% |
*Note: Anthropic reports SWE-bench Verified; OpenAI reports SWE-bench Pro Public. These are different benchmark variants. Direct comparison is not fully valid.
The overall pattern is clear: Claude Opus 4.6 leads on reasoning-heavy benchmarks like GPQA Diamond and MMLU Pro, while GPT-5.3 Codex dominates terminal and computer-use workloads.
Reasoning and Mathematics
| Benchmark | Claude Opus 4.6 | GPT-5.3 Codex | Gemini 3 Pro |
|---|---|---|---|
| AIME 2025 (math) | 92.8% | 100% | 95.0% |
| ARC-AGI-2 | 68.8% | — | 31.1% |
| GPQA Diamond | 77.3% | — | — |
| MMLU Pro | 85.1% | — | — |
| GDPval-AA (knowledge work) | 1,606 Elo | ~1,462 Elo | — |
Pure mathematical reasoning shows clear stratification: GPT models achieve 100% on AIME 2025, while Claude reaches 92.8% and Gemini 3 Pro matches closely at 95.0%.
A particularly striking result: Opus 4.6 nearly doubled Opus 4.5's score on ARC-AGI-2, reaching 68.8% versus 37.6% for the previous generation.
Knowledge Work and Enterprise Tasks
For enterprise knowledge work, Claude Opus 4.6's 1,606 Elo score on GDPval-AA puts it 144 points ahead of GPT-5.2 on economically valuable tasks in finance, legal, and professional domains.
Thinking Modes Explained
All three models now offer advanced reasoning modes. These let the model "think" longer before answering — useful for hard problems, but it costs more tokens and time.
| Thinking Feature | Claude Opus 4.6 | GPT-5.3 Codex | Gemini 3 Pro |
|---|---|---|---|
| Mode Name | Adaptive Thinking | High-compute reasoning | Deep Think |
| Effort Levels | 4 (low, medium, high, max) | Single toggle | Single toggle |
| Interleaved Thinking | Yes (between tool calls) | Partial | Yes |
| Token Cost Impact | High on max effort | High | High |
| Best Use | Complex multi-step tasks | Terminal loops | Scientific/math tasks |
Adaptive thinking replaces Claude's previous extended thinking, with four effort levels that let Claude dynamically decide when deeper reasoning helps, with "high" set as the default.
In code quality testing, Gemini 3 Pro posted the highest rate of control flow mistakes at 200 per million lines of code — nearly four times higher than Opus 4.6 Thinking's 55 per million lines.
Real-World Coding Performance
Benchmarks only tell part of the story. Here is what independent testing found when developers ran these models on actual production tasks.
Code Quality and Security
Opus 4.6 Thinking leads in functional performance with an 83.62% pass rate. However, this comes with high verbosity — generating over 600,000 lines of code to solve the benchmark test. Gemini 3 Pro achieves a comparable 81.72% pass rate while maintaining low cognitive complexity and low verbosity, suggesting a unique ability to solve complex problems with concise, readable code.
On security, the gap between models is significant:
| Security Metric | Claude Opus 4.6 | GPT-5.3 Codex | Gemini 3 Pro |
|---|---|---|---|
| Control flow mistakes | 55/MLOC | 22/MLOC (lowest) | 200/MLOC (highest) |
| Blocker vulnerabilities | 44/MLOC | Lowest | — |
| Code verbosity | Very high | Very high | Low |
Agentic Coding: What Developers Experienced
Real-world developer testing found that Opus 4.6 has a higher ceiling as a model but also higher variance — it is more parallelized by default and more creative. GPT-5.3 Codex, meanwhile, is fast, reliable, and autonomous, but does not quite reach the same heights on the hardest open-ended tasks.
For quick, focused tasks like fixing a null pointer exception, GPT-5.3 Codex wins on speed. For finding vulnerabilities across a 20,000-line codebase, Claude Opus 4.6 wins because long context enables finding issues spanning multiple files. For implementing authentication across frontend, backend, and database, Claude Opus 4.6's Agent Teams parallelizes work effectively.
Unique Features: What Sets Each Model Apart
Claude Opus 4.6 — The Agent Teams Model
Claude Opus 4.6 features a 1M token context window, hybrid reasoning that allows instant or extended thinking, and a new Agent Teams feature that enables parallel multi-agent coordination. In one documented case, 16 agents built a 100,000-line compiler working in parallel.
Key exclusive features:
- Agent Teams for parallel multi-agent workflows
- Adaptive Thinking with four controllable effort levels
- Compaction API for infinite-length conversations
- 128K max output tokens
- Native Excel and PowerPoint integration
GPT-5.3 Codex — The Speed and Computer-Use Model
GPT-5.3 Codex merges frontier coding performance with professional knowledge into a single unified model that runs 25% faster than its predecessor. OpenAI positions it as a full computer-use agent — not just a code autocomplete tool, but a system that can debug, deploy, monitor, write PRDs, edit copy, run tests, and analyze metrics across terminals, IDEs, browsers, and desktop apps.
Key exclusive features:
- 25% faster inference than GPT-5.2
- Self-bootstrapping sandboxes
- Deep diffs and interactive steering
- Classified as "High capability" for cybersecurity — the first OpenAI model with this rating
- OSWorld-leading computer use capabilities
Gemini 3 Pro — The Multimodal Value Model
Gemini 3 Pro is the clear winner for cost-efficiency and native video analysis, making it the ideal engine for processing massive amounts of multimedia data.
Key exclusive features:
- 2M token context window (largest of the three)
- Native video processing — unique among most models
- 24-language voice input
- 81.0% on MMMU-Pro for multimodal understanding
- Lowest pricing at $2/$12 per million tokens
Context Window: Advertised vs. Actual Performance
The advertised context window and the usable context window are very different things.
| Model | Advertised Context | MRCR v2 Score at 1M Tokens | Verdict |
|---|---|---|---|
| Claude Opus 4.6 | 1M (beta) | 76% | Usable at scale |
| GPT-5.3 Codex | 400K | — | Standard for most tasks |
| Gemini 3 Pro | 2M | 26.3% at 1M | Large window, lower retrieval accuracy |
Claude Opus 4.6 offers 1 million tokens that actually work, scoring 76% on MRCR v2 long-context retrieval. Gemini 3 Pro advertises 2 million tokens but scores only 26.3% on the same test at 1 million tokens. Usable context beats advertised context.
Pricing Comparison
| Cost Scenario | Claude Opus 4.6 | GPT-5.3 Codex | Gemini 3 Pro |
|---|---|---|---|
| Input (per 1M tokens) | $5 ($7.50 over 200K) | TBD | $2 |
| Output (per 1M tokens) | $25 ($37.50 over 200K) | TBD | $12 |
| Annual API cost (est.) | ~$150,000 | TBD | ~$70,000 |
| Subscription (consumer) | $20/month (Pro) | Via ChatGPT Plus | $20/month (Advanced) |
Note: GPT-5.3 Codex API token pricing had not been published as of February 5, 2026. Check OpenAI's pricing page for current rates before making budget decisions.
Claude Opus 4.6's token efficiency partially offsets its higher base rate. At medium effort level, Opus matches peak performance while consuming significantly fewer output tokens, which can translate to 50–75% cost reductions at scale in agentic workflows.
Which Model Should You Use? A Task-by-Task Guide
| Use Case | Best Choice | Why |
|---|---|---|
| Complex software engineering | Claude Opus 4.6 | Highest SWE-bench scores, Agent Teams, best long-context code review |
| Terminal / command-line automation | GPT-5.3 Codex | 77.3% Terminal-Bench 2.0, fastest inference |
| Computer use / desktop automation | GPT-5.3 Codex | OSWorld leadership, native desktop support |
| Enterprise knowledge work | Claude Opus 4.6 | 144 Elo point lead on GDPval-AA |
| Mathematical reasoning | GPT-5.3 Codex | 100% AIME 2025 |
| Video and multimodal tasks | Gemini 3 Pro | Only model with native video processing |
| Long document processing | Gemini 3 Pro | 2M context, best for massive corpora |
| Budget-sensitive teams | Gemini 3 Pro | 60% cheaper than Claude output pricing |
| Multi-agent parallel workflows | Claude Opus 4.6 | Agent Teams, no equivalent in other models |
| Security audits across large codebases | Claude Opus 4.6 | Best vulnerability detection per MLOC |
Safety and Alignment
All three companies have raised their safety bars alongside capability improvements, but with different philosophies.
Claude Opus 4.6 ships with Constitutional AI v3 and ASL-3 protocols, while GPT-5.3 is the first OpenAI model classified as "High capability" for cybersecurity tasks and the first directly trained to identify software vulnerabilities.
Gemini 3 Pro includes Google's Responsible AI frameworks and content filtering systems aligned with Google DeepMind's safety research program.
The Smart Strategy: Model Routing
The AI leaders in 2026 are not picking one model. They are routing tasks to the right model.
A model routing approach — using Claude for coding and enterprise work, GPT-5.3 for mathematical reasoning, and Gemini for multimodal tasks — can deliver better results at 70–80% lower cost compared to single-model deployment.
A practical routing framework:
| Task Type | Route To |
|---|---|
| Production code, agents, enterprise docs | Claude Opus 4.6 |
| Math, abstract reasoning, fast terminal tasks | GPT-5.3 Codex |
| Video, images, high-volume, research corpora | Gemini 3 Pro |
| Simple queries, cost-sensitive volume | Gemini 3 Flash or Claude Sonnet 4.6 |
One important note for Claude users: Claude Sonnet 4.6, released February 17, 2026, scores 79.6% on SWE-bench — within 1% of Opus 4.6 — while costing 40% less. It is now the default model for Claude Code Free and Pro users. For many coding tasks, Sonnet 4.6 is the better value.
Conclusion
As of February 19, 2026, the frontier model race is the closest it has ever been — and more specialized than ever.
Claude Opus 4.6 is the strongest all-around choice for software engineering teams, enterprise knowledge work, and complex multi-agent workflows. Its Agent Teams feature has no equivalent, and its long-context reliability is the best tested.
GPT-5.3 Codex wins on raw speed, terminal proficiency, computer use, and mathematical reasoning. If you run high-volume agentic loops or need autonomous desktop interaction, it leads.
Gemini 3 Pro offers the best price-to-performance ratio for most general and multimodal tasks. For video processing, document-heavy research, and cost-sensitive deployments, it is the clear choice.
The winners in 2026 will not be those who pick the "best" model. They will be those who know which model to use for which job — and route accordingly.