📖 18 min read
AI Coding April 2026 Head-to-Head
Best AI for Writing Code 2026: Claude vs GPT-5 vs Gemini vs DeepSeek – 10 Real Coding Tests With Results
We ran 10 real-world coding tasks through four frontier AI models and scored every output. No vibes. No benchmarks pulled from a leaderboard. Just code that either works or doesn’t.
TL;DR
Claude Opus 4.6 wins the overall shootout with 87/100 total points. It dominates debugging, refactoring, and complex architecture tasks. GPT-5.4 finishes second at 78/100 with the best generalist consistency. Gemini 3 Deep Think scores 66/100 and surprises on algorithmic reasoning but struggles with output limits. DeepSeek V4 hits 63/100 – impressive for a model costing one-tenth the price. If you’re paying for one coding AI, Claude is the answer. If you’re on a budget, DeepSeek is the story of the year. Not sure which AI is worth paying for? We broke that down too.
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The Setup: How We Tested
Every AI coding benchmark on the internet has the same problem: they test toy problems. FizzBuzz. Two Sum. Sorting algorithms. Nobody ships that code.
So we designed 10 tests that reflect what developers actually do at work. In a separate article, we built the same app with each tool to compare the full development experience. Here, we focus on raw model capability across 10 tasks: building APIs, debugging async nightmares, refactoring code that makes you want to quit, converting between languages, and writing CI pipelines that don’t break at 2am.
Each test was run through four models:
- Claude Opus 4.6 – via Claude Code CLI and API
- GPT-5.4 – via ChatGPT and API
- Gemini 3 Deep Think – via Google AI Studio
- DeepSeek V4 – via API
Same prompt for every model. No chain-of-thought tricks, no “you are an expert” preambles. Just the task description and relevant context. Every output was tested by running the actual code, not by eyeballing it.
Scoring: Each test is scored out of 10 across three dimensions – correctness (does it work?), code quality (is it clean, idiomatic, well-structured?), and completeness (did it handle edge cases, error handling, documentation?). The score shown is a weighted average: 50% correctness, 30% quality, 20% completeness.
The 10 Tests
01 Build a REST API with Auth (Python/FastAPI)
The task: Build a FastAPI REST API with JWT authentication, user registration, login, and three CRUD endpoints for a “notes” resource. Include password hashing, token refresh, and proper error responses. Use SQLite with SQLAlchemy.
Claude Opus 4.6: Delivered a complete, runnable application in a single response. Proper project structure with separate routers, models, schemas, and a dependency injection pattern for the database session. Used passlib with bcrypt for hashing and python-jose for JWT. Included token refresh. All 14 endpoint tests passed on first run. The code read like it was written by a senior backend dev.
Score: 9/10
GPT-5.4: Also produced a working API, but crammed everything into a single file. Auth worked correctly. Missed token refresh initially – when the prompt specifically asked for it. CRUD endpoints were functional but lacked pagination. 12/14 tests passed; needed a small fix for the missing refresh endpoint.
Score: 7/10
Gemini 3 Deep Think: Took a more academic approach. Spent a significant chunk of its output explaining the architecture before writing code. The code itself was clean but used Pydantic V1 syntax despite V2 being the standard for over a year. Token refresh was implemented. 11/14 tests passed due to minor import issues.
Score: 7/10
DeepSeek V4: Functional but minimal. Auth worked. CRUD worked. No pagination, no token refresh, inconsistent error response format. However – it produced the output 3x faster than any competitor. 10/14 tests passed.
Score: 6/10
02 Debug a Tricky Async Race Condition
The task: Given a 200-line Node.js file with an intentional race condition in a concurrent queue processor (shared counter mutation without proper locking, plus a subtle timing bug in the retry logic), identify and fix all concurrency issues.
Claude Opus 4.6: Identified both bugs within the first paragraph of its response. Explained the race condition with a step-by-step timeline showing how two concurrent workers could corrupt the counter. Fixed it using a mutex pattern with async-mutex. Also caught the retry timing bug that none of the other models found – a setTimeout that could fire after the queue was already drained. Textbook debugging.
Score: 10/10
GPT-5.4: Found the main race condition and applied a correct fix using atomic operations. Missed the retry timing bug entirely. The explanation was clear but less detailed than Claude’s. The fix worked for the primary issue.
Score: 7/10
Gemini 3 Deep Think: This is where Deep Think mode earned its name. It walked through every possible interleaving of the async operations, produced a truth table of states, and correctly identified both bugs. However, the fix it proposed for the retry bug introduced a new edge case. Close, but not quite.
Score: 8/10
DeepSeek V4: Found the race condition. Applied a workable fix. Explanation was thin – more “here’s the fix” than “here’s why.” Missed the retry bug.
Score: 6/10
03 Write a React Component with State Management
The task: Build a kanban board component (like a simplified Trello) with drag-and-drop between columns, persistent state via localStorage, undo/redo, and keyboard accessibility. Use React 19 with hooks – no external state library.
Claude Opus 4.6: Solid implementation. Used useReducer for state with a proper action history for undo/redo. Drag-and-drop worked using the HTML Drag and Drop API. localStorage sync was handled with a custom hook. Keyboard nav worked. The component was split into logical sub-components. One minor issue: the undo stack didn’t cap its size, so it could grow indefinitely.
Score: 8/10
GPT-5.4: Nearly identical quality to Claude on this one. Also used useReducer. The drag-and-drop implementation was slightly more polished with better visual feedback during drags. Undo/redo worked and included a cap of 50 actions. Keyboard accessibility was present but less thorough – no aria labels on the drop zones.
Score: 8/10
Gemini 3 Deep Think: Went overboard with abstraction. Created a custom state management layer that was essentially a mini Redux. The code worked but was harder to read and maintain than it needed to be. Drag-and-drop was functional. No undo/redo – it restructured the state in a way that made history tracking complex, then ran out of output length before implementing it.
Score: 6/10
DeepSeek V4: Clean, minimal implementation. Drag-and-drop worked. Used useState instead of useReducer, which made the undo/redo implementation messy. localStorage worked. No keyboard accessibility whatsoever.
Score: 7/10
04 SQL Query Optimization on a Complex Schema
The task: Given a PostgreSQL schema with 8 tables (e-commerce system – users, orders, items, products, categories, reviews, inventory, shipping), optimize a slow report query that joins 6 tables, uses multiple subqueries, and takes 12 seconds on a dataset of 2M rows. Provide the optimized query and explain what indexes to add.
Claude Opus 4.6: Rewrote the query using CTEs to eliminate redundant subqueries, identified three missing composite indexes, and suggested a partial index on the orders table for the date range filter. Estimated the query would drop from 12s to under 400ms. When tested against the actual dataset, it came in at 380ms. Also suggested a materialized view for the report if it runs daily.
Score: 9/10
GPT-5.4: Good optimization. Identified the key missing indexes and simplified the subqueries. Didn’t suggest the partial index. Optimized query ran in about 900ms – solid improvement but not as aggressive as Claude’s rewrite. Explanation was excellent and would be easy for a junior dev to follow.
Score: 8/10
Gemini 3 Deep Think: This was Gemini’s strongest test. Deep Think mode essentially simulated the query planner, walking through each join and estimating cardinality. Its index recommendations were the most thorough of any model – it suggested five indexes including two that even Claude missed (though they had marginal impact). Optimized query: 450ms.
Score: 9/10
DeepSeek V4: Identified the obvious missing indexes and removed one redundant subquery. The optimization was correct but conservative. Query time dropped to about 2.1 seconds – better, but left performance on the table.
Score: 7/10
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05 Convert a Python Script to Rust
The task: Convert a 300-line Python data processing script (CSV parsing, filtering, aggregation, and JSON output) to idiomatic Rust. The Python script uses pandas. The Rust version should use serde and csv crates.
Claude Opus 4.6: Produced idiomatic Rust that compiled on the first try. Used proper error handling with thiserror, defined clean structs with serde derives, and the CSV processing used iterators elegantly. Performance was 40x faster than the Python original. One small miss: it didn’t implement the equivalent of pandas’ groupby as efficiently as it could have – used a nested loop where a HashMap would have been better.
Score: 8/10
GPT-5.4: The Rust compiled after fixing two minor issues (a lifetime annotation and a missing trait bound). The code was functional but read like “Python translated to Rust” rather than idiomatic Rust. Used .unwrap() in several places instead of proper error handling. Still achieved 30x speedup over Python.
Score: 7/10
Gemini 3 Deep Think: Did not compile. Three errors related to borrow checker issues – a classic sign that the model understands Rust syntax but not ownership semantics at a deep level. After fixing the errors manually (took about 10 minutes), the code ran correctly and was reasonably idiomatic.
Score: 5/10
DeepSeek V4: Surprisingly strong. Compiled with one minor fix (a missing use statement). The code was clean, used proper Result types throughout, and the HashMap approach for grouping was actually the most efficient of all four models. DeepSeek’s Rust training data appears to be excellent.
Score: 8/10
06 Write Unit Tests for Existing Code
The task: Given a 400-line TypeScript module (a payment processing service with Stripe integration, retry logic, idempotency handling, and webhook validation), write comprehensive unit tests using Vitest. Mock external dependencies appropriately.
Claude Opus 4.6: Wrote 23 tests covering happy paths, error cases, retry behavior, idempotency conflicts, and webhook signature validation. The mocking strategy was clean – used vi.mock at the module level and vi.spyOn for specific assertions. Every test ran and passed. Found an actual bug in the provided code (the idempotency key wasn’t being hashed consistently) and wrote a test that exposed it.
Score: 9/10
GPT-5.4: Wrote 18 tests. Good coverage of the main flows. The mocking approach was more verbose – created manual mock classes instead of using Vitest’s built-in mocking. All tests passed. Didn’t catch the idempotency bug. Test descriptions were very readable.
Score: 8/10
Gemini 3 Deep Think: Wrote 15 tests. Coverage was adequate but focused too heavily on happy paths – only 3 error case tests out of 15. Used the correct Vitest APIs. Two tests had assertion errors due to incorrect expected values (it misread the retry delay calculation in the source code).
Score: 6/10
DeepSeek V4: Wrote 12 tests. All passed. The tests were correct but shallow – mostly “does this function return without throwing?” style assertions rather than deep behavioral checks. Mocking was minimal. Would give you a green CI badge but not much confidence in the code.
Score: 6/10
07 Refactor Spaghetti Code into Clean Architecture
The task: Given a 600-line “god file” Express.js application (routes, business logic, database queries, validation, and error handling all in one file), refactor it into a clean architecture with proper separation of concerns. Maintain identical API behavior.
Claude Opus 4.6: This was Claude’s strongest showing. It produced a complete refactored project with controllers, services, repositories, middleware, validators, and error handlers – each in its own file. The dependency injection pattern was clean. It created an index file that wired everything together. Every original API endpoint worked identically after the refactor. It even added a brief comment at the top of each file explaining its responsibility. The kind of refactor you’d get from a staff engineer, not an AI.
Score: 10/10
GPT-5.4: Good refactor. Separated routes, controllers, and models. Didn’t go as deep on the service/repository pattern – business logic lived in the controllers, which is technically a step down from a full clean architecture. But the code was much more maintainable than the original. All endpoints worked.
Score: 8/10
Gemini 3 Deep Think: Spent a lot of output tokens explaining what clean architecture is before producing code. The actual refactor was incomplete – it showed the structure and implemented about 60% of the files, then hit output limits. What it did produce was well-structured.
Score: 5/10
DeepSeek V4: Decent separation into routes and controllers. Kept database queries inline in the controllers. Validation was moved to middleware, which was a nice touch. Not a full clean architecture, but a meaningful improvement. All endpoints worked.
Score: 7/10
08 Build a CLI Tool from a Spec Document
The task: Given a 2-page spec for a CLI tool that manages local dev environments (start/stop services, check ports, tail logs, and show status dashboards), build it in Go. Should use cobra for CLI parsing and support both JSON and table output formats.
Claude Opus 4.6: Solid CLI tool. Used cobra correctly with proper subcommands, flags, and help text. The service management logic was well-implemented using os/exec. JSON and table output both worked. The status dashboard used a simple table format rather than a TUI, which was a reasonable interpretation of “dashboard.” Built and ran without errors.
Score: 8/10
GPT-5.4: Also used cobra. Very similar quality to Claude’s output. The difference was in polish – GPT added colored output using fatih/color, progress spinners for long operations, and a --verbose flag. Built on first try. The extra UX touches made it feel more like a real tool.
Score: 9/10
Gemini 3 Deep Think: Built the tool in Go but didn’t use cobra – rolled its own argument parser. This was a direct contradiction of the spec. The custom parser worked but didn’t generate help text or handle flag combinations well. The core functionality was correct.
Score: 6/10
DeepSeek V4: Used cobra. Clean implementation. Missing the log tailing feature entirely – it just printed “not implemented” for that subcommand. The rest worked. JSON output had a formatting issue with nested objects.
Score: 6/10
09 Fix a CSS Layout Bug from a Screenshot
The task: Given a screenshot of a broken layout (overlapping cards, broken flexbox on mobile, z-index stacking issues on a modal, and a footer that floats over content), plus the HTML/CSS source, fix all visual issues to match a provided design mockup.
Claude Opus 4.6: Identified all four issues from the screenshot and source code. The flexbox fix was correct (flex-wrap: wrap with proper min-width on children). The z-index fix used a proper stacking context. The footer was fixed with a sticky footer pattern. All fixes were minimal and targeted – didn’t rewrite things that weren’t broken. Matched the mockup.
Score: 9/10
GPT-5.4: Fixed three of four issues. Missed the z-index stacking context problem – applied a higher z-index to the modal without creating a new stacking context on the parent, which wouldn’t actually fix the issue in all browsers. The other three fixes were correct.
Score: 7/10
Gemini 3 Deep Think: Strong on the visual analysis. Identified all four issues and provided correct fixes for each. The explanations referenced specific CSS spec behavior, which was impressive. However, it also “fixed” two things that weren’t broken, introducing a small regression in the header spacing.
Score: 7/10
DeepSeek V4: Fixed the flexbox issue and the footer. Didn’t address the overlapping cards or z-index problem. The fixes it did apply were correct. Limited visual reasoning compared to the multimodal-native models.
Score: 5/10
10 Write a Complete GitHub Actions CI/CD Pipeline
The task: Write a GitHub Actions workflow for a monorepo (React frontend + Python backend + shared protobuf definitions). Requirements: lint, test, build Docker images, push to ECR, deploy to staging on PR merge, deploy to production on release tag. Include caching, concurrency controls, and Slack notifications.
Claude Opus 4.6: Produced a multi-file workflow setup: a reusable workflow for the build/test matrix, separate deploy workflows for staging and production, and a shared action for Slack notifications. Used proper path filters so only changed packages triggered their builds. Caching was thorough (pip, node_modules, Docker layers). Concurrency groups prevented duplicate deploys. The YAML was valid and would work as-is after plugging in secrets.
Score: 7/10 – Lost points because the reusable workflow pattern, while elegant, was more complex than necessary for the stated requirements.
GPT-5.4: Single workflow file with a job matrix. Clean, readable, and practical. Path filters worked. Caching was present for all three package managers. Concurrency controls were correct. Slack notification used a simple curl to a webhook rather than an action, which is actually more reliable. Production deploy required manual approval via an environment protection rule. This was the most “ready to ship” pipeline of the four.
Score: 9/10
Gemini 3 Deep Think: Comprehensive but over-engineered. Created separate workflows for each package in the monorepo (good) but also added a dependency graph resolver workflow that would determine build order (unnecessary for this use case). The YAML was valid. Caching was present. No Slack notification was included despite the spec requiring it.
Score: 7/10
DeepSeek V4: Functional single-file workflow. Covered lint, test, build, and deploy. No path filters – every push triggered all builds. Caching was present for node_modules only. Slack notification worked. Concurrency control was missing. Would work but would waste a lot of CI minutes in a busy repo.
Score: 5/10
The Scoreboard
| Test | Claude Opus 4.6 | GPT-5.4 | Gemini 3 DT | DeepSeek V4 |
|---|---|---|---|---|
| 01 – REST API with Auth | 9 | 7 | 7 | 6 |
| 02 – Async Race Condition | 10 | 7 | 8 | 6 |
| 03 – React Component | 8 | 8 | 6 | 7 |
| 04 – SQL Optimization | 9 | 8 | 9 | 7 |
| 05 – Python to Rust | 8 | 7 | 5 | 8 |
| 06 – Unit Tests | 9 | 8 | 6 | 6 |
| 07 – Refactor Spaghetti | 10 | 8 | 5 | 7 |
| 08 – CLI Tool from Spec | 8 | 9 | 6 | 6 |
| 09 – CSS Layout Fix | 9 | 7 | 7 | 5 |
| 10 – CI/CD Pipeline | 7 | 9 | 7 | 5 |
| TOTAL (out of 100) | 87 | 78 | 66 | 63 |
Overall Winner: Claude Opus 4.6 with 87/100. It didn’t win every test, but it was the most consistently excellent across the board. It was the only model to score a perfect 10 on any test – and it did it twice.
Best For… Recommendations
Best for Debugging and Refactoring: Claude Opus 4.6
If your day involves reading other people’s code, finding bugs, and cleaning up messes, Claude is the clear choice. Its ability to identify subtle issues (like that async retry timing bug) and produce clean architectural refactors is ahead of the pack. Use it through Claude Code for the best experience – the agentic loop lets it iterate on its own output.
Best for Practical DevOps and Tooling: GPT-5.4
GPT-5.4 won the CI/CD test and the CLI tool test. It has a knack for producing code that feels “production-ready” with attention to UX details like colored output, progress indicators, and sensible defaults. If you’re building internal tools or setting up infrastructure, GPT is your best bet.
Best for Algorithmic Deep Thinking: Gemini 3 Deep Think
When Gemini’s Deep Think mode locks onto a reasoning problem – like the SQL optimization or the async race condition analysis – it produces the most thorough analysis of any model. The problem is consistency. It over-engineers, hits output limits, and sometimes ignores parts of the spec. Use it for the hard thinking, not the implementation.
Best Bang for Buck: DeepSeek V4
DeepSeek’s Rust conversion was as good as Claude’s. Its overall scores are within striking distance of models that cost 5-10x more per token. If you’re a solo developer or a startup watching every dollar, DeepSeek is genuinely competitive for straightforward coding tasks. Just don’t rely on it for complex debugging or architecture.
Cost Comparison
We tracked the token usage and cost for each test. Here’s what it looks like if you’re paying via API, which is how most developers should be using these tools:
| Model | Avg. Cost Per Test | Total (10 Tests) | Est. Monthly (Heavy Use) | Monthly Subscription |
|---|---|---|---|---|
| Claude Opus 4.6 | $0.42 | $4.20 | $80-150 | $20 Pro / $100 Max |
| GPT-5.4 | $0.28 | $2.80 | $50-100 | $20 Plus / $200 Pro |
| Gemini 3 Deep Think | $0.35 | $3.50 | $60-120 | $20 Ultra |
| DeepSeek V4 | $0.04 | $0.40 | $8-15 | N/A (API only) |
The DeepSeek pricing is almost absurd. At roughly one-tenth the cost of the big three, the question isn’t whether DeepSeek is as good – it’s whether the quality gap is worth 10x the price. For many tasks, honestly, it’s not.
For subscription pricing, Claude’s $100/month Max plan gives you heavy-use access to Opus 4.6 and is the best deal for a professional developer who uses AI coding tools all day. GPT-5.4’s $200 Pro plan is hard to justify unless you specifically need its strengths. Gemini’s $20 Ultra is a steal if you can work within the rate limits.
What Surprised Us
DeepSeek’s Rust quality. We expected DeepSeek to lag behind on systems programming languages. Instead, it produced one of the cleanest Rust implementations in the entire test suite. Its training data for Rust must be exceptionally well-curated.
Gemini’s output limit problem. Deep Think mode produces excellent reasoning – but it burns through tokens on explanations before getting to the code. On two tests (the React component and the refactoring task), it ran out of space before finishing the implementation. This is a fixable problem (higher output limits are coming), but right now it’s a real constraint.
Claude’s consistency. No model scored below 7 except… actually, Claude did score a 7 on the CI/CD test. But that’s its lowest. Across 10 wildly different coding tasks, it never had a bad test. That kind of reliability matters when you’re building a workflow around an AI tool.
GPT-5.4’s UX instincts. Multiple times, GPT added polish that no other model thought to include – colored terminal output, progress spinners, environment protection rules for production deploys. It codes like a product-minded engineer, not just an algorithm solver. The upcoming GPT-5.5 + Codex combo could push this even further.
The Limitations of This Test
Transparency matters, so here’s what this test doesn’t tell you:
- Multi-turn performance. All tests were single-shot. In practice, you iterate with the AI over multiple rounds. Claude Code’s agentic loop gives it an advantage in real usage that this test doesn’t capture.
- Context window usage. None of our tests pushed past 50K tokens of context. If you’re feeding in entire codebases, Gemini’s 1M token window is a massive differentiator that doesn’t show up here.
- Speed. DeepSeek was consistently the fastest by a wide margin. If your workflow involves dozens of small queries per hour, response time matters a lot.
- Model updates. These results are from April 2026. All four models will improve. Scores from three months ago already look different from today.
The Verdict: If You Can Only Pick One
For Professional Developers: Claude Opus 4.6
If you write code for a living and want the single best AI coding assistant, Claude Opus 4.6 through Claude Code is the answer in April 2026. It scored highest overall, excels at the hardest tasks (debugging, refactoring, architecture), and its worst performance was still good. The $100/month Max plan or Claude Code subscription pays for itself if it saves you even two hours of debugging per month.
For Budget-Conscious Developers: DeepSeek V4
If you’re spending your own money and need to be careful, DeepSeek V4 at one-tenth the cost is an incredible value. Use it for code generation, boilerplate, and straightforward tasks. When you hit something complex, switch to Claude or GPT for that specific problem.
The Smart Play: Use Two
The real power move is using Claude as your primary (for debugging, refactoring, and complex tasks) and DeepSeek as your secondary (for boilerplate, quick questions, and high-volume work). Your combined monthly cost stays under $120 and you get the best of both worlds. GPT-5.4 is the best alternative primary if Claude’s style doesn’t click with you. Gemini is worth watching – if they fix the output limits, Deep Think mode could be a game-changer.
AI coding assistants have gone from “novelty” to “which one do I put on the company card” in about 18 months. All four models tested here can write functional code. The differences show up in the hard parts – the debugging, the architecture, the edge cases. That’s where your choice of tool actually matters.
We’ll re-run this test in July 2026 when the next wave of model updates drops. If previous patterns hold, the scores will be tighter. The gap is closing. But for right now, the data says what it says.
Related Reading
- I Built the Same App With Claude Code, Cursor, Copilot, and Devin – Full development experience compared
- GPT-5.5 + Codex: The Stack Nobody Saw Coming – How the new combo changes the game
- Gemini vs Claude vs GPT: Which AI Is Worth Paying For? – Beyond coding – the full comparison
- Best AI Agents 2026: Which One Actually Works? – From coding assistants to autonomous agents
Published on BetOnAI.net – April 29, 2026. All tests conducted during the week of April 21-25, 2026. Models tested at their default settings via official APIs and interfaces. No system prompts were used beyond the task descriptions. Full test prompts and outputs are available on request.
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