Claude Opus 4.5 vs GPT-5 for Coding: Benchmarks, Pricing & Real Tests (2026)
February 2026 comparison — Claude Opus 4.5 vs GPT-5 for coding. SWE-bench scores, API pricing ($5 vs $1.25/M input), real-world code generation tests. Which is better for your development workflow?
In February 2026, two models sit at the top of the AI coding hierarchy: Anthropic’s Claude Opus 4.5 and OpenAI’s GPT-5. Both can generate production-quality code, debug complex issues, refactor legacy systems, and architect entire applications from a natural-language description. But they are not interchangeable. They differ sharply in pricing, context handling, benchmark strengths, and the kind of developer workflow they best support.
This guide is written for API developers who need to make a practical decision: which model should power your coding assistant, CI pipeline, or agentic development workflow? We compare benchmarks, break down cost-per-task, test both models on the same coding problems, and recommend the optimal stack for different budgets.
Quick Comparison Table
| Feature | Claude Opus 4.5 | GPT-5 |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Input Price | $5.00/M tokens | $1.25/M tokens |
| Output Price | $25.00/M tokens | $10.00/M tokens |
| Context Window | 200K tokens | 400K tokens |
| Max Output | 64K tokens | 64K tokens |
| SWE-bench Verified | ~72% | ~69% |
| HumanEval | 95%+ | 94%+ |
| MBPP | ~90% | ~89% |
| Multimodal | Vision | Vision + Audio |
| Extended Thinking | Yes | Yes (via o3) |
| Prompt Caching | 90% savings on cache hits | 50% savings on cache hits |
| Batch API | 50% off (async) | 50% off (async) |
Both models support structured output (JSON mode), function calling, and streaming. Both have vision capabilities for reading screenshots, diagrams, and handwritten notes. The major differentiators are price (GPT-5 is 4x cheaper on input), context window (GPT-5 offers 2x more), and raw coding benchmark scores (Claude holds a slight edge).
Coding Benchmark Comparison
Benchmarks are not everything, but they provide a standardized baseline. Here is how the two flagship models compare on the most relevant coding benchmarks as of February 2026.
SWE-bench Verified (Real-World Bug Fixes)
SWE-bench Verified tests whether a model can resolve real GitHub issues from popular open-source repositories. This is the closest benchmark to what developers actually do: reading a codebase, understanding a bug report, and producing a working patch.
| Model | SWE-bench Verified Score |
|---|---|
| Claude Opus 4.5 | ~72% |
| GPT-5 | ~69% |
| Claude Sonnet 4.5 | ~70% |
| o3 (reasoning) | ~71% |
Claude Opus 4.5 leads by roughly 3 percentage points. This gap is meaningful in practice — it translates to Opus successfully resolving about 1 in 10 additional issues that GPT-5 fails on. For teams that rely on AI to auto-fix bugs in CI pipelines or handle pull request reviews, this edge matters.
Notably, Claude Sonnet 4.5 scores ~70%, nearly matching Opus at a fraction of the cost. And OpenAI’s reasoning model o3 reaches ~71% by using chain-of-thought, though at higher effective token cost due to internal reasoning tokens.
HumanEval (Function-Level Code Generation)
HumanEval measures the ability to generate correct Python functions from docstrings. Both models have essentially saturated this benchmark:
| Model | HumanEval Score |
|---|---|
| Claude Opus 4.5 | 95.7% |
| GPT-5 | 94.3% |
| Claude Sonnet 4.5 | 94.8% |
The difference here is negligible. Both models can reliably generate correct function implementations for well-specified problems. HumanEval is no longer a useful differentiator at the frontier.
MBPP (Mostly Basic Python Programming)
MBPP tests basic programming tasks — the kind of code a junior developer writes daily:
| Model | MBPP Score |
|---|---|
| Claude Opus 4.5 | ~90% |
| GPT-5 | ~89% |
| Claude Sonnet 4.5 | ~89% |
Again, near parity. For everyday coding tasks — writing utility functions, data transformations, string manipulation — both models are equally reliable.
LiveCodeBench (Competitive Programming)
LiveCodeBench uses recent competitive programming problems to test algorithmic reasoning. This benchmark is harder to game because it continuously adds new problems:
| Model | LiveCodeBench Pass Rate |
|---|---|
| Claude Opus 4.5 | ~45% |
| GPT-5 | ~43% |
| o3 (reasoning) | ~52% |
For pure algorithmic problem-solving, OpenAI’s o3 reasoning model pulls ahead due to its chain-of-thought architecture. But among the standard (non-reasoning) flagships, Claude Opus holds a slight lead. Neither model is close to solving competitive programming reliably — this remains a frontier challenge.
Benchmark Summary
Claude Opus 4.5 holds a consistent but narrow lead across coding benchmarks, with the most meaningful advantage on SWE-bench Verified (real-world bug fixes). For function generation and basic programming, the models are at parity. If you need maximum algorithmic reasoning, OpenAI’s o3 is the better choice, but it is a different model with different pricing.
Pricing Comparison for Coding Tasks
Benchmarks tell you which model produces better code. Pricing tells you whether you can afford it. Here the story shifts dramatically in GPT-5’s favor.
Per-Token Pricing
| Model | Input (per 1M) | Output (per 1M) | Effective Cost Ratio |
|---|---|---|---|
| Claude Opus 4.5 | $5.00 | $25.00 | Baseline |
| GPT-5 | $1.25 | $10.00 | 4x cheaper input, 2.5x cheaper output |
| Claude Sonnet 4.5 | $3.00 | $15.00 | 40% cheaper than Opus |
| GPT-4.1 | $2.00 | $8.00 | 2.5x cheaper input, 3x cheaper output |
Cost Per Coding Interaction
A typical coding interaction — sending a code snippet with context and receiving a fix or implementation — uses roughly 2,000 input tokens and 1,000 output tokens. At that volume:
| Model | Cost per Request | Requests per $1 |
|---|---|---|
| Claude Opus 4.5 | $0.035 | 28 |
| GPT-5 | $0.0125 | 80 |
| Claude Sonnet 4.5 | $0.021 | 47 |
| GPT-4.1 | $0.012 | 83 |
| GPT-5 Mini | $0.0025 | 400 |
| DeepSeek V3.2 | $0.0016 | 625 |
GPT-5 delivers 2.8x more coding requests per dollar than Claude Opus 4.5. For many developers, that efficiency gap outweighs the ~3% benchmark advantage.
Monthly Cost at Scale (1,000 Coding Requests Per Day)
If you are running an AI-assisted development workflow with roughly 1,000 coding requests per day (a realistic figure for a small team or an agentic coding tool):
| Model | Daily Cost | Monthly Cost (30 days) |
|---|---|---|
| Claude Opus 4.5 | $35.00 | $1,050 |
| GPT-5 | $12.50 | $375 |
| Claude Sonnet 4.5 | $21.00 | $630 |
| GPT-4.1 | $12.00 | $360 |
| GPT-5 Mini | $2.50 | $75 |
| DeepSeek V3.2 | $1.60 | $48 |
At 1,000 requests per day, Claude Opus 4.5 costs $1,050/month — nearly 3x the GPT-5 bill. For a solo developer or a startup watching burn rate, that difference funds an entire additional engineer’s tool budget.
Cost Optimization: Prompt Caching and Batch API
Both providers offer ways to reduce these costs:
Anthropic prompt caching reduces input costs by 90% on cache hits. If your coding assistant uses a consistent system prompt (which most do), the effective input cost of Opus drops from $5.00 to $0.50/M for cached tokens. This narrows the gap with GPT-5 significantly.
OpenAI cached input pricing offers 50% off for cached tokens, bringing GPT-5 input to $0.625/M on cache hits.
Batch APIs from both providers give 50% off for asynchronous workloads — useful for batch code review, test generation, or migration tasks that do not need real-time responses.
With aggressive prompt caching, the effective monthly cost comparison shifts:
| Model | Monthly (No Caching) | Monthly (With Caching) |
|---|---|---|
| Claude Opus 4.5 | $1,050 | ~$825 |
| GPT-5 | $375 | ~$330 |
| Claude Sonnet 4.5 | $630 | ~$490 |
Even with caching, Claude Opus remains 2.5x more expensive than GPT-5. The cost gap is structural.
Real-World Coding Strengths
Benchmarks and pricing set the floor. Real-world coding tasks reveal where each model genuinely excels — and where it falls short.
Where Claude Opus 4.5 Excels
Complex multi-file refactoring. Claude Opus 4.5 is exceptionally good at understanding the relationships between files in a large codebase and producing coordinated changes across them. When you ask it to “extract this service layer into a separate module and update all imports,” it reliably gets the cross-file references right. GPT-5 handles this too, but Opus makes fewer mistakes on edge cases like circular dependencies and re-exports.
Following detailed coding conventions. If your team has a style guide — specific naming conventions, error-handling patterns, documentation standards — Opus follows them more consistently. It pays closer attention to system prompt instructions and deviates less over long conversations. This makes it particularly strong for teams that use tools like Cursor or Claude Code with detailed .cursorrules or AGENTS.md files.
Extended thinking for architecture decisions. With extended thinking enabled, Opus can reason through architectural tradeoffs before writing code. Ask it to “design the database schema for a multi-tenant SaaS app” and it will consider normalization, query patterns, scaling implications, and migration strategies before producing SQL. GPT-5 tends to jump to a solution faster, which is sometimes good but occasionally misses important considerations.
Generating well-documented code. Opus produces more thorough docstrings, inline comments, and README content. For open-source projects or teams where code readability is a priority, this matters.
Where GPT-5 Excels
Larger context window (400K tokens). GPT-5’s 400K context window is double Claude’s 200K. For tasks that involve processing an entire codebase in a single prompt — such as “find all security vulnerabilities in this repository” or “generate a migration plan for this legacy system” — GPT-5 can ingest twice as much code without truncation. If you regularly work with repositories over 100K tokens, this advantage is significant.
Multimodal debugging. GPT-5 accepts images and audio alongside text. You can paste a screenshot of an error dialog, a photo of a whiteboard architecture diagram, or even a voice recording describing a bug — and GPT-5 will incorporate it into its response. Claude has vision capabilities too, but GPT-5’s multimodal integration is more mature, especially for audio inputs.
Broader framework and language coverage. GPT-5’s training data appears to cover more niche frameworks, older languages, and less common libraries. If you are working with Elixir/Phoenix, Rust’s async ecosystem, or legacy COBOL systems, GPT-5 is more likely to have relevant training data. Claude Opus is strong across mainstream languages but can be thinner on edge cases.
Cost efficiency at scale. At 4x cheaper input tokens and 2.5x cheaper output tokens, GPT-5 is simply more practical for high-volume use cases. If you are running an agentic loop that makes 50-100 API calls per coding task, the cost difference compounds quickly.
The Claude Sonnet 4.5 Secret
Here is a fact that many developers overlook: for most coding tasks, you do not need Opus at all. Claude Sonnet 4.5 delivers approximately 95% of Opus’s coding quality at 40% less cost.
| Metric | Opus 4.5 | Sonnet 4.5 | Gap |
|---|---|---|---|
| SWE-bench Verified | ~72% | ~70% | 2 percentage points |
| HumanEval | 95.7% | 94.8% | <1 percentage point |
| Input Price | $5.00/M | $3.00/M | 40% cheaper |
| Output Price | $25.00/M | $15.00/M | 40% cheaper |
Sonnet 4.5 also supports extended thinking, prompt caching, and batch processing. For the vast majority of coding tasks — generating functions, writing tests, debugging, code review — it is indistinguishable from Opus in practice.
The same logic applies on the OpenAI side. GPT-4.1 ($2.00/$8.00 per 1M tokens) with its 1 million token context window is arguably a better coding model than GPT-5 for large codebase tasks, and it costs more per token but handles dramatically more context.
For developers who want the “Anthropic quality” without the Opus price tag, Sonnet 4.5 is the answer. For those who want maximum context at a reasonable price, GPT-4.1 is the OpenAI equivalent.
Best AI Coding Stack for 2026
No single model is optimal for every coding task. The most cost-effective approach is a tiered stack that routes requests to the right model based on task complexity.
Recommended Stack
| Task Type | Recommended Model | Cost per Request | Why |
|---|---|---|---|
| Complex architecture & refactoring | Claude Opus 4.5 | ~$0.035 | Highest code quality, best at multi-file changes |
| Daily coding assistant | Claude Sonnet 4.5 or GPT-5 | ~$0.013-$0.021 | Best balance of quality and cost |
| Quick completions & test generation | GPT-5 Mini ($0.25/$2) or DeepSeek V3.2 ($0.27/$1.10) | ~$0.002 | Fast, cheap, good enough for simple tasks |
| Large codebase analysis | GPT-4.1 ($2/$8, 1M context) | ~$0.012 | Million-token context for full repo processing |
| Algorithmic problem-solving | o3 ($2/$8) | ~$0.012 | Best reasoning model for complex algorithms |
| Code review | Claude Sonnet 4.5 or GPT-5 | ~$0.013-$0.021 | Both excel at identifying issues |
Monthly Budget Breakdown
For a team of 5 developers using the tiered stack above, with roughly 500 total requests per day distributed across tiers:
| Tier | % of Requests | Model | Monthly Cost |
|---|---|---|---|
| Complex (10%) | 50/day | Opus 4.5 | $52 |
| Standard (50%) | 250/day | Sonnet 4.5 | $158 |
| Quick (30%) | 150/day | GPT-5 Mini | $11 |
| Large context (10%) | 50/day | GPT-4.1 | $18 |
| Total | $239/month |
Compare this to using a single model for everything:
| Single-Model Approach | Monthly Cost |
|---|---|
| All Opus 4.5 | $525 |
| All GPT-5 | $188 |
| All Sonnet 4.5 | $315 |
| Tiered stack | $239 |
The tiered approach costs less than all-Sonnet while delivering Opus-level quality on the tasks that need it. If you swap the standard tier from Sonnet to GPT-5, the total drops to about $200/month.
Code Example: Same Task, Both APIs
To make this comparison concrete, here is the same coding task sent to both APIs. The task: implement a rate limiter class in TypeScript using the sliding window algorithm.
Claude Opus 4.5 (Anthropic API)
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-opus-4-5-20250220",
max_tokens=2048,
messages=[
{
"role": "user",
"content": (
"Implement a SlidingWindowRateLimiter class in TypeScript. "
"Requirements: configurable window size and max requests, "
"thread-safe for concurrent access, includes cleanup of "
"expired entries, and exports a factory function. "
"Include JSDoc comments and unit test examples."
)
}
]
)
print(message.content[0].text)
# Cost: ~2,100 input tokens + ~1,500 output tokens
# = (2100 * $5 + 1500 * $25) / 1,000,000 = $0.048GPT-5 (OpenAI API)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-5",
max_tokens=2048,
messages=[
{
"role": "user",
"content": (
"Implement a SlidingWindowRateLimiter class in TypeScript. "
"Requirements: configurable window size and max requests, "
"thread-safe for concurrent access, includes cleanup of "
"expired entries, and exports a factory function. "
"Include JSDoc comments and unit test examples."
)
}
]
)
print(response.choices[0].message.content)
# Cost: ~2,100 input tokens + ~1,500 output tokens
# = (2100 * $1.25 + 1500 * $10) / 1,000,000 = $0.018Both models produce correct, well-structured implementations. In our tests, Claude Opus 4.5 tends to produce more thorough JSDoc comments and includes edge cases in the test examples (such as testing exact boundary conditions and cleanup timing). GPT-5 produces slightly more concise code and occasionally uses newer TypeScript features. The quality difference is marginal — but the cost difference is 2.7x.
When to Choose Claude Opus 4.5
Pick Claude Opus 4.5 when:
-
Code quality is non-negotiable. For production code that will be maintained for years, Opus’s attention to edge cases, documentation, and coding conventions pays off. The 3% SWE-bench advantage translates to fewer bugs that slip through automated code generation.
-
You rely on system prompts and conventions. If your workflow depends on the model faithfully following a detailed system prompt (common with Cursor, Claude Code, or custom IDE integrations), Opus follows instructions more precisely than GPT-5 across long conversations.
-
You need extended thinking for architecture. When the task is “design the module structure for this feature” rather than “write this function,” Opus’s extended thinking mode produces more thoughtful, considered architectural decisions.
-
Your budget allows it. If you are an enterprise team where developer time costs $100+/hour and a 5% improvement in AI code quality saves even one hour of debugging per week, the $675/month premium over GPT-5 pays for itself many times over.
When to Choose GPT-5
Pick GPT-5 when:
-
Budget is a primary concern. At $0.0125 per coding request versus $0.035, GPT-5 is the clear choice for cost-sensitive teams, indie developers, and high-volume workloads.
-
You need large context windows. Processing entire repositories, long specification documents, or extensive code histories benefits from GPT-5’s 400K token context. For even larger contexts, GPT-4.1 offers 1 million tokens at $2/$8.
-
You work with niche technologies. GPT-5’s broader training data coverage means better results for uncommon languages, legacy systems, and specialized frameworks.
-
You use multimodal inputs. Debugging from screenshots, interpreting UI mockups, or processing architecture diagrams alongside code is smoother with GPT-5’s more mature multimodal pipeline.
-
You are building agentic workflows. Agentic coding tools that make dozens of API calls per task amplify cost differences. At 50 calls per task, Opus costs $1.75 per task versus GPT-5’s $0.63 — a difference that adds up to thousands per month at team scale.
Bottom Line
The answer depends on what you optimize for.
If budget matters most: GPT-5 wins. It delivers 97% of Claude Opus 4.5’s coding quality at 36% of the cost. For most developers and most tasks, that tradeoff is easy to accept.
If code quality is paramount: Claude Opus 4.5 wins. Its consistent edge on SWE-bench, superior instruction following, and more thorough code generation make it the best model available for developers who need the highest possible output quality and can afford the premium.
For most developers: Claude Sonnet 4.5 is the sweet spot. It delivers ~95% of Opus’s coding quality at 60% of the cost ($3/$15 vs $5/$25), and it outperforms GPT-5 on SWE-bench while costing only about 1.7x more. If you want the Anthropic quality advantage without the Opus price tag, Sonnet 4.5 is the answer.
The smartest approach in 2026 is not to choose one model — it is to build a tiered stack. Use Opus for your hardest 10% of tasks, Sonnet or GPT-5 for the bulk of daily work, and GPT-5 Mini or DeepSeek V3.2 for quick completions and tests. This gives you maximum quality where it matters and minimum cost where it does not.
Related tools and guides:
- AI Model Pricing Calculator — Compare 25+ models side by side with your own usage patterns
- AI Token Counter — Count tokens before you send API requests
- Claude API Pricing Guide 2026 — Full Opus vs Sonnet vs Haiku breakdown
- OpenAI API Pricing Guide 2026 — GPT-5, GPT-4.1, o3, and batch discounts
- Full AI API Pricing Comparison 2026 — All 7 providers compared