TL;DR: The Real Answer
The Short Answer
- For speed and code review: GPT-5.3 Codex. 25% faster execution, 2-4x fewer tokens, leads Terminal-Bench 2.0 at 77.3%. Best when you need a model that finds edge cases and ships quick fixes.
- For reasoning and large codebases: Claude Opus 4.6. 1M token context, 80.8% SWE-bench Verified, +144 Elo advantage on knowledge work. Best when you need a model that understands vague intent and handles 10+ file refactors.
- For value: Claude Sonnet 4.6. 79.6% SWE-bench at $3/$15 per million tokens. 98% of Opus quality at 60% of the price.
- For open source: Qwen 2.5 Coder 32B or DeepSeek V3.1. GPT-4o level performance, runs locally.
- The real answer: The harness matters more than the model. SWE-Bench Pro proves it.
Both GPT-5.3 Codex and Claude Opus 4.6 are frontier coding models. The gap between them is smaller than the gap between either one and a bad prompt. If you are choosing between them, you are already in good shape. The question is not which model is "better" but which philosophy matches how you write code.
The February 5 Double Drop
February 5, 2026 was the most significant simultaneous release in AI coding history. OpenAI shipped GPT-5.3 Codex. Anthropic shipped Claude Opus 4.6. Same day, different bets.
GPT-5.3 Codex: The Executor
OpenAI doubled down on terminal execution. Codex 5.3 leads Terminal-Bench 2.0 at 77.3%, runs 25% faster than its predecessor, and uses 2-4x fewer tokens than Opus on equivalent tasks. It finally picked up some of Opus's warmth — less robotic, more willing to just do things without detailed specifications. Pricing: $6/$30 per million tokens.
Claude Opus 4.6: The Reasoner
Anthropic pushed reasoning depth. Opus 4.6 scores 80.8% on SWE-bench Verified, leads GPQA Diamond and MMLU Pro reasoning benchmarks, and offers a 1M token context window (beta) — 4x Codex's 256K. It handles vague prompts where Codex needs babysitting. Pricing: $5/$25 per million tokens.
The convergence is real. Interconnects called this the "post-benchmark era" — the fine margins between these models will be felt in many model versions this year. Opus 4.6 has the precision that made Codex the go-to for hard coding tasks. Codex 5.3 has the fluency that made Claude the go-to for greenfield development. The personality gap is narrowing. The workflow gap is not.
| Dimension | GPT-5.3 Codex | Claude Opus 4.6 |
|---|---|---|
| SWE-bench Verified | ~80% | 80.8% |
| Terminal-Bench 2.0 | 77.3% | 65.4% |
| Context window | 256K tokens | 1M tokens (beta) |
| MRCR v2 (1M context) | N/A | 76% |
| Knowledge work Elo | Baseline | +144 over GPT-5.2 |
| Speed vs predecessor | 25% faster | Standard |
| Pricing (input/output per 1M) | $6 / $30 | $5 / $25 |
Head-to-Head: The Race Card
Numbers tell part of the story. But developers choose models based on feel as much as benchmarks. Here is how Codex 5.3 and Opus 4.6 compare across seven dimensions that matter in daily coding — speed, reasoning, intent understanding, token efficiency, multi-file refactoring, code review, and context window.
Head-to-Head: The Race Card
GPT-5.3 Codex vs Claude Opus 4.6 across 7 dimensions
Scores based on benchmarks, developer surveys, and hands-on testing as of February 2026. Neither model "wins" overall — it depends on your workflow.
The pattern is clear: Codex wins on execution dimensions (speed, token efficiency, code review). Opus wins on understanding dimensions (reasoning, intent, multi-file refactoring, context). Neither dominates. Your workflow determines which dimensions matter most.
"Switching from Opus 4.6 to Codex 5.3 feels like I need to babysit the model in terms of more detailed descriptions when doing somewhat mundane tasks." — Nathan Lambert, Interconnects
Benchmark Reality Check
Every comparison article throws benchmark numbers at you. Here is what they actually mean — and where they break down.
SWE-bench Verified: The Industry Standard
SWE-bench Verified tests whether a model can resolve real GitHub issues from open-source projects. It is the most cited coding benchmark, but the scores have plateaued at the frontier. The top five models are within 1.3 percentage points of each other.
| Model | Score | Notes |
|---|---|---|
| Claude Opus 4.5 | 80.9% | Previous Anthropic flagship |
| Claude Opus 4.6 | 80.8% | Current Anthropic flagship |
| GPT-5.3 Codex | ~80% | Current OpenAI flagship |
| Claude Sonnet 4.6 | 79.6% | Best value option |
| DeepSeek V3.1 | 66.0% | Best open-source |
| Gemini 2.5 Pro | 63.8% | Best for web dev |
Why SWE-bench Verified Matters Less Than You Think
At 80%+, models are solving the "easy" issues reliably. The remaining 20% are genuinely hard — ambiguous specs, multi-repository dependencies, performance optimizations that require deep domain knowledge. The gap between 80.8% and 80.0% is noise. The gap between using a model with a good agent scaffold vs. a bare API call is 20+ percentage points.
SWE-Bench Pro: Where the Harness Matters
SWE-Bench Pro is harder and more realistic. The scores are lower, the variance is higher, and the scaffold matters enormously. Scale AI runs all models with an uncapped cost budget and a 250-turn limit.
| Rank | Model | Score |
|---|---|---|
| 1 | Claude Opus 4.5 | 45.89% |
| 2 | Claude Sonnet 4.5 | 43.60% |
| 3 | Gemini 3 Pro Preview | 43.30% |
| 4 | Claude Sonnet 4 | 42.70% |
| 5 | GPT-5 (High) | 41.78% |
| 6 | GPT-5.2 Codex | 41.04% |
| 7 | Claude 4.5 Haiku | 39.45% |
| 8 | Qwen 3 Coder 480B | 38.70% |
| 9 | MiniMax 2.1 | 36.81% |
| 10 | Gemini 3 Flash | 34.63% |
Notice what is missing: GPT-5.3 Codex is not yet on the SWE-Bench Pro leaderboard (it will be soon). But look at the distribution. Claude models take four of the top seven slots. The Anthropic scaffold is consistently strong. Gemini 3 Pro Preview at #3 is the dark horse — Google's coding models are underrated.
Terminal-Bench 2.0: The DevOps Test
Terminal-Bench tests a model's ability to use a live terminal for system administration, environment management, and CLI workflows. This is where Codex dominates.
This is Codex's strongest differentiator. If your workflow is heavily terminal-based — DevOps, infrastructure as code, CI/CD pipeline debugging — Codex has a meaningful edge. The 11.9 percentage point gap is not noise. It reflects Codex's optimization for command-line interaction patterns.
The Harness Matters More Than the Model
This is the most important section of this article. If you take away one thing, make it this: the agent scaffold, IDE, and tooling around a model determine more of its coding performance than the model weights themselves.
SWE-Bench Pro proves this. The same model scores 23% with a basic SWE-Agent scaffold and 45%+ with a sophisticated multi-turn scaffold that has a 250-turn budget. That 22+ point swing dwarfs the difference between any two frontier models.
IDE Matters
Cursor, Windsurf, and VS Code with extensions each wrap models differently. The same Opus 4.6 performs differently in Cursor Composer vs. Claude Code terminal vs. a raw API call. IDE context retrieval, file indexing, and agent orchestration are the multiplier.
Agent Design Matters
Claude Code scores 80.9% on SWE-bench — higher than raw Opus 4.6 in most agent frameworks. The difference is Anthropic's agent engineering: tool use patterns, retry logic, context management, and the chain of reasoning built into the harness.
Prompt Engineering Matters
Codex needs more specific prompts for routine tasks. Opus handles vague intent better. This means the same developer with the same task gets different results depending on their prompting style. The 'best model' is the one that matches how you communicate.
The Implication
Stop optimizing for which model to use. Start optimizing for how you use it. A mid-tier model in a great agent harness beats a frontier model in a bad one. This is why tools like WarpGrep (semantic codebase search for terminal agents) and well-configured IDE setups matter more than switching from Opus to Codex or back.
GPT-5.3 Codex: The Speed-First Philosophy
Codex is built for developers who think in terminals. Its philosophy: execute fast, use minimal tokens, iterate quickly. Here is where it genuinely excels and where it falls short.
Where Codex Dominates
Terminal Execution
77.3% on Terminal-Bench 2.0 means Codex handles git operations, package management, CI/CD debugging, and system administration better than any other model. Git branching — which used to break older models — now works reliably.
Code Review and Edge Cases
Developers consistently report Codex finds bugs that Opus misses. Its pattern: scan the full diff, identify edge cases, suggest targeted fixes. Less verbose, more surgical. This makes it the better choice for pre-merge code review.
Token Efficiency
On a Figma-to-code task: Codex used 1.5M tokens. Opus used 6.2M. On a job scheduler task: Codex used 72K tokens, Opus used 234K. Codex thinks less, ships faster. If you're paying per token, this 2-4x efficiency gap compounds fast.
Speed
25% faster than GPT-5.2. In practice, Codex completes agentic tasks in roughly half the wall-clock time of Opus. For rapid prototyping and iteration — where you want five attempts in the time Opus takes for two — this speed advantage is real.
Where Codex Falls Short
Codex struggles with ambiguity. Give it a vague prompt like "refactor this to be cleaner" and it will ask clarifying questions or make conservative changes. Opus interprets intent and makes bold moves. Codex also has a 256K context window — workable for most tasks, but limiting for massive monorepos where Opus's 1M context lets it see the full picture.
The personality difference is noticeable. One developer on Interconnects described switching from Opus to Codex as "needing to babysit the model with more detailed descriptions for mundane tasks." Codex is precise but literal. It does what you say, not what you mean.
Claude Opus 4.6: The Depth-First Philosophy
Opus is built for developers who think in systems. Its philosophy: understand deeply, plan thoroughly, execute with confidence. Here is where it genuinely excels and where it falls short.
Where Opus Dominates
Intent Understanding
Give Opus a vague prompt and it infers what you actually want. 'Make this component accessible' becomes a full ARIA implementation with keyboard navigation, screen reader support, and focus management. Codex would ask you to specify which accessibility standards.
Multi-file Refactoring
Opus handles 10+ file refactors where changes cascade across modules, types, tests, and documentation. Its 1M context window means it can hold the entire dependency graph in memory. This is its strongest real-world advantage over Codex.
Reasoning Depth
Opus leads GPQA Diamond, MMLU Pro, and TAU-bench reasoning benchmarks. When the task requires understanding why code exists — not just what it does — Opus produces better architectural decisions. It thinks before it codes.
Long-Context Coherence
76% on MRCR v2 at 1M context (Sonnet 4.5 scores 18.5%). For codebases where you need the model to understand distant relationships — a type defined in one file, used in another, tested in a third — Opus maintains coherence where Codex drops context.
Where Opus Falls Short
Opus is expensive in tokens. It "thinks out loud" — providing explanations, asking follow-up questions, documenting its reasoning. On a Figma cloning task, it used 6.2M tokens where Codex used 1.5M. If you are paying per token and running hundreds of tasks per day, this 4x cost difference is significant.
Opus is also slower. Its thoroughness comes at the cost of wall-clock time. Lenny's Newsletter documented shipping 93,000 lines of code in 5 days using both models — Opus generated more lines per session (~1,200 in 5 minutes) but Codex's iterations were faster and more targeted (~200 lines in 10 minutes, but with fewer tokens and less rework).
Token Economics: The Hidden Cost
Per-token pricing is misleading. What matters is cost per task. Codex and Opus have similar per-token rates but radically different token consumption patterns.
| Task | Codex Tokens | Opus Tokens | Codex Cost | Opus Cost |
|---|---|---|---|---|
| Job scheduler implementation | 72,579 | 234,772 | ~$2.40 | ~$7.05 |
| Figma-to-code clone | 1,499,455 | 6,232,242 | ~$54 | ~$187 |
| Bug fix (typical) | ~15,000 | ~45,000 | ~$0.60 | ~$1.50 |
| Multi-file refactor (10 files) | ~120,000 | ~280,000 | ~$4.80 | ~$8.40 |
For a team running 50 coding tasks per day, the token efficiency gap compounds to thousands of dollars per month. But the cost calculation is not that simple. Opus's thoroughness means fewer retries, fewer regressions, and fewer "it worked but broke something else" moments.
The Sonnet 4.6 Sweet Spot
Claude Sonnet 4.6 scores 79.6% on SWE-bench Verified — within 1.2 points of Opus 4.6 — at $3/$15 per million tokens (40% cheaper than Opus). For teams that want Claude's reasoning style without Opus-level costs, Sonnet 4.6 is the clear value pick. It handles 80%+ of coding tasks at Opus-level quality.
The Full Landscape: Every Model Worth Considering
Codex and Opus dominate the conversation, but they are not the only options. Here is every model worth considering for coding in 2026, including open-source alternatives and specialists.
| Model | Best For | Key Metric |
|---|---|---|
| Claude Opus 4.6 | Complex reasoning, large codebases, multi-file refactoring | 80.8% SWE-bench, 1M context |
| GPT-5.3 Codex | Terminal execution, code review, speed | 77.3% Terminal-Bench, 2-4x token efficient |
| Claude Sonnet 4.6 | Best value for near-frontier coding | 79.6% SWE-bench, $3/$15 per M tokens |
| Gemini 2.5 Pro | Web dev, long context, front-end | #1 WebDev Arena, 91.5% at 128K context |
| Gemini 3 Pro Preview | Agentic coding at scale | 43.30% SWE-Bench Pro (#3) |
| DeepSeek V3.1 | Open-source, self-hosted | 66% SWE-bench Verified |
| Qwen 2.5 Coder 32B | Open-source, local deployment | GPT-4o level, 40+ languages |
| Qwen 3 Coder 480B | Open-source frontier | 38.70% SWE-Bench Pro |
| Claude Sonnet 4 | Budget with good quality | 42.70% SWE-Bench Pro (#4) |
The Google Dark Horse
Gemini models are underrated in the coding conversation. Gemini 2.5 Pro leads WebDev Arena — the benchmark for building functional, aesthetic web apps — and handles 1M context natively with 91.5% accuracy at 128K and 83.1% at 1M. Gemini 3 Pro Preview sits at #3 on SWE-Bench Pro, ahead of GPT-5 and GPT-5.2 Codex. If you are building web applications, Gemini deserves a serious look.
The Open-Source Tier
Qwen 2.5 Coder 32B matches GPT-4o across 40+ programming languages and runs on consumer hardware. DeepSeek V3.1 at 66% SWE-bench Verified competes with models that cost 10-100x more per token. Qwen 3 Coder 480B at 38.70% on SWE-Bench Pro is within striking distance of frontier proprietary models. For teams with data sovereignty requirements or who want to avoid per-token costs, the open-source tier is legitimate.
Decision Framework: Pick Your Model in 60 Seconds
Answer these questions honestly. The model picks itself.
| Your Situation | Best Model | Why |
|---|---|---|
| Large codebase (100K+ lines) | Claude Opus 4.6 | 1M context window, multi-file refactoring |
| Terminal-heavy workflow (DevOps, infra) | GPT-5.3 Codex | 77.3% Terminal-Bench, CLI-native |
| Code review before merge | GPT-5.3 Codex | Finds edge cases, surgical fixes |
| Greenfield feature development | Claude Opus 4.6 | Interprets vague intent, bold architecture |
| Budget-conscious team | Claude Sonnet 4.6 | 98% of Opus quality, 40% cheaper |
| Web/front-end development | Gemini 2.5 Pro | #1 WebDev Arena, 1M native context |
| Data sovereignty / self-hosted | Qwen 2.5 Coder 32B | GPT-4o level, runs locally |
| Maximum autonomy (fire and forget) | Claude Code (Opus 4.6) | 80.9% SWE-bench, best agent scaffold |
| Rapid prototyping and iteration | GPT-5.3 Codex | 25% faster, 2-4x fewer tokens per cycle |
| Enterprise, compliance-heavy | Claude (any tier) | Anthropic safety guarantees, 1M context |
If you are a VS Code user working on a mid-size project without compliance requirements, either Codex or Opus will work well. Try both. The model that matches your prompting style — detailed and specific (Codex) vs. vague and intent-driven (Opus) — is the right one.
The Emerging Hybrid Workflow
The most productive developers in 2026 are not choosing between Codex and Opus. They are using both — plus a terminal agent — and routing tasks to the model that handles them best. This is not theoretical. Lenny's Newsletter, ChatPRD, and multiple Reddit threads document developers shipping 44+ PRs per week using this approach.
Opus for Generation
Use Opus 4.6 or Sonnet 4.6 for new feature development, architecture decisions, and multi-file refactoring. Its intent understanding and 1M context mean less back-and-forth on complex, ambiguous tasks.
Codex for Review
Route code review, edge case detection, and pre-merge checks to Codex 5.3. Its precision, token efficiency, and pattern matching catch bugs that Opus's more expansive style overlooks.
Terminal Agent for Autonomy
Claude Code (80.9% SWE-bench) handles fully autonomous operations: test generation, migration scripts, CI fixes. It uses the same Opus reasoning in a purpose-built agent scaffold optimized for multi-step terminal workflows.
| Task | Route To | Why |
|---|---|---|
| New feature (greenfield) | Opus 4.6 | Intent understanding, bold architecture choices |
| Bug fix (known cause) | Codex 5.3 | Fast, token-efficient, surgical |
| Code review | Codex 5.3 | Finds edge cases, less verbose |
| Multi-file refactor | Opus 4.6 | 1M context, cascading changes |
| Test generation | Claude Code | Autonomous, agent-optimized scaffold |
| DevOps / CI pipeline | Codex 5.3 | 77.3% Terminal-Bench |
| Front-end / web app | Gemini 2.5 Pro | #1 WebDev Arena |
| Codebase exploration | WarpGrep + any model | Semantic search, model-agnostic |
Making Hybrid Work Practical
The hybrid workflow only works if switching between models is fast. Terminal agents make this easy — they let you swap the underlying model with a flag. Tools like WarpGrep add semantic codebase search to any terminal agent, so you can route the search task to the best retrieval system regardless of which model generates the code. The model is a component of your stack, not your entire stack.
Frequently Asked Questions
What is the best AI model for coding in 2026?
There is no single best model. GPT-5.3 Codex leads Terminal-Bench 2.0 (77.3%) and uses 2-4x fewer tokens. Claude Opus 4.6 leads reasoning benchmarks with a 1M token context window and 80.8% SWE-bench Verified. Both are frontier models. The best model depends on your workflow: Codex for speed and code review, Opus for complex multi-file reasoning and greenfield architecture.
Is Claude or GPT better for coding?
Claude Opus 4.6 excels at complex reasoning, multi-file refactoring, and understanding vague developer intent. GPT-5.3 Codex excels at speed, terminal execution, code review, and token efficiency. Claude Sonnet 4.6 (79.6% SWE-bench, $3/$15 per million tokens) is the best value for near-frontier coding. The 2025 Stack Overflow survey shows GPT at 82% overall usage but Claude at 45% among professional developers — reflecting Claude's strength on harder tasks.
What are the SWE-bench scores for all major models?
On SWE-bench Verified: Opus 4.5 (80.9%), Opus 4.6 (80.8%), Codex 5.3 (~80%), Sonnet 4.6 (79.6%), DeepSeek V3.1 (66%), Gemini 2.5 Pro (63.8%). On SWE-Bench Pro: Opus 4.5 (45.89%), Sonnet 4.5 (43.60%), Gemini 3 Pro (43.30%), Sonnet 4 (42.70%), GPT-5 (41.78%), Codex 5.2 (41.04%), Qwen 3 Coder (38.70%).
Is Gemini 2.5 Pro good for coding?
Yes. Gemini 2.5 Pro leads WebDev Arena for building web applications, handles 1M context natively with 91.5% accuracy at 128K, and scores 63.8% on SWE-bench Verified. Gemini 3 Pro Preview sits at #3 on SWE-Bench Pro (43.30%). For front-end development and large codebases, Gemini is a strong contender.
What is the best open-source model for coding?
Qwen 2.5 Coder 32B matches GPT-4o across 40+ languages and runs locally. DeepSeek V3.1 scores 66% on SWE-bench Verified. Qwen 3 Coder 480B scores 38.70% on SWE-Bench Pro, competing with proprietary frontier models. For teams needing data sovereignty or zero per-token cost, these are production-ready options.
How much does it cost to use Codex vs Opus?
Per-token: Codex is $6/$30 per million (input/output). Opus is $5/$25. But Codex uses 2-4x fewer tokens per task, making it cheaper in practice. A Figma cloning task: Codex ~$54, Opus ~$187. Sonnet 4.6 at $3/$15 per million tokens is the best value for near-frontier quality.
Does the model or the coding agent matter more?
The agent matters more. SWE-Bench Pro shows a 22+ point swing between basic and optimized scaffolds using the same model. Claude Code (80.9% SWE-bench) outperforms raw Opus in most agent frameworks because the harness — tool use, retry logic, context management — is the multiplier. Optimize your tooling before optimizing your model choice.
Stop Debating Models. Start Searching Codebases.
WarpGrep adds semantic codebase search to any terminal agent — works with Codex, Opus, Sonnet, or any model. The harness matters more than the model.