# Claude Opus 4.7 vs GPT-5.5 for Coding (May 2026): SWE-bench, Pricing, Verified

> Published 2026-05-07 · https://www.promptzone.com/arjun_srinivasan/claude-opus-47-vs-gpt-55-for-coding-may-2026-swe-bench-pricing-verified-3mhm

> **Quick navigation:** [TL;DR](#tldr) · [SWE-bench](#swebench) · [Pricing](#pricing) · [Long context](#context) · [Tool use](#tools) · [Side-by-side](#table) · [Pick by use case](#pick) · [FAQ](#faq) · [Sources](#sources)

In May 2026 there are two model families that 80% of professional engineering teams reach for: Claude (Anthropic) and GPT-5.x (OpenAI). After a quiet January and February, both vendors shipped major updates within a week of each other in April: Claude **Opus 4.7** on April 16, and **GPT-5.5** on April 23.

This is the head-to-head for coding work specifically, with verified figures from the SWE-bench Verified leaderboard and vendor pricing pages.

## TL;DR {#tldr}

- **GPT-5.5 leads SWE-bench Verified at 88.7%**, narrowly ahead of Claude Opus 4.7 (Adaptive) at 87.6%. The gap is real but small.
- **Claude is cheaper for output-heavy work**: Opus 4.7 at $5/$25 per M vs GPT-5.5 at $5/$30 per M. For high-output agent loops, Claude wins on cost.
- **Sonnet 4.6 is the value pick**: $3/$15 with 1M-token context. Most teams use Sonnet daily and reach for Opus only on hard problems.
- **GPT-5 mini and nano are dramatically cheaper** for bulk inference: $0.25/$2 and $0.05/$0.40 per M tokens.

For most professional engineering work in 2026: **Sonnet 4.6 daily, Opus 4.7 for hard problems, GPT-5.5 for the highest-difficulty SWE-bench-style tasks**.

## SWE-bench Verified (May 2026 leaderboard) {#swebench}

| Rank | Model | Score |
|---|---|---|
| 1 | GPT-5.5 (OpenAI, April 23, 2026) | **88.7%** |
| 2 | Claude Opus 4.7 (Adaptive) (Anthropic, April 16, 2026) | **87.6%** |
| – | GPT-5.3 Codex | 85.0% |
| – | Claude Opus 4.5 | 80.9% |
| – | Claude Opus 4.6 | 80.8% |
| – | DeepSeek V4-Pro (open-weight 1.6T MoE) | 80.6% |
| – | Gemini 3.1 Pro | 80.6% |

Reading the gap: 1.1 percentage points on a benchmark with measurement noise is small. In day-to-day coding the differences show up more in *workflow fit* than raw capability.

## Pricing (verified May 2026) {#pricing}

**Anthropic Claude API:**

| Model | Input ($/M) | Output ($/M) | Context |
|---|---|---|---|
| Haiku 4.5 | $1.00 | $5.00 | 200K |
| Sonnet 4.6 | $3.00 | $15.00 | 1M |
| Opus 4.7 | $5.00 | $25.00 | 200K (1M tier separate) |

- **Prompt caching**: 90% discount on cached input
- **Batch processing**: 50% off all tokens
- Note: Opus 4.7 ships with a new tokenizer that can produce up to 35% more tokens for the same input compared to Opus 4.6 — effective cost-per-request can be higher

**OpenAI GPT-5.x API:**

| Model | Input ($/M) | Output ($/M) |
|---|---|---|
| GPT-5.5 | $5.00 | $30.00 |
| GPT-5.5-pro | $30.00 | $180.00 |
| GPT-5.1 Standard | $1.25 | $10.00 |
| GPT-5 Mini | $0.25 | $2.00 |
| GPT-5-nano | $0.05 | $0.40 |

- **GPT-5.5 above 272K input tokens**: 2× input / 1.5× output for the full session
- Regional/data-residency endpoints: 10% uplift on GPT-5.5
- Prompt caching: 90% discount on cached input

**Cost reading**: For typical agent loops with prompt caching:
- Sonnet 4.6 at $3 input is meaningfully cheaper than GPT-5.5 at $5 input
- Opus 4.7 vs GPT-5.5: $5 input each, but Opus output is $25 vs GPT-5.5 output $30 — Claude wins on output-heavy work
- For bulk inference: GPT-5-nano at $0.05/$0.40 is the cheapest credible coding model in either lineup

## Long context {#context}

**Claude Sonnet 4.6:** 1M-token context at standard pricing. **Opus 4.7**: 200K standard, 1M-tier available separately. **Haiku 4.5**: 200K.

**GPT-5.5:** 272K standard before pricing changes; full max higher with the >272K-token surcharge.

For monorepo-scale work, **Sonnet 4.6 at 1M tokens** is the strongest default in the lineup — same context as the 200K tiers, four to five times the headroom.

## Tool use & agent workflows {#tools}

Both vendors ship excellent function-calling. The execution differences:

**Claude:**
- More reliable JSON tool-call output; rare malformed calls in practice
- Native integration with [Claude Code](/elena_rodriguez_16a03695/claude-2026-the-complete-developer-guide-to-models-api-claude-code-and-mcp-1n3p) — Anthropic's CLI agent
- **MCP (Model Context Protocol)** is Anthropic-led, an open standard with a reference implementation at modelcontextprotocol.io; broadest tool ecosystem in 2026

**OpenAI GPT-5.x:**
- Excellent function-calling for narrow APIs
- ChatGPT advanced data analysis remains best-in-class for one-off Python work
- No first-party CLI agent equivalent to Claude Code in 2026 (third-parties fill the gap)

For build-your-own-agent work in 2026, MCP plus tool-call reliability still tilt most teams toward Claude.

## IDE & CLI ecosystem {#ecosystem}

This is where the choice often happens for engineers in practice.

**Claude appears in:**
- **Claude Code** (Anthropic's CLI agent — strongest agentic tool in 2025-2026)
- **Cursor** (selectable; Cursor lists Sonnet 4.5 and Opus 4.6 alongside its own Composer model)
- **GitHub Copilot Pro+** added Claude Opus 4.6 access in March 2026 ($39/mo tier)
- Cline, Aider, Continue (BYO model)
- Web claude.ai with Projects and Artifacts

**GPT-5.x appears in:**
- **GitHub Copilot** (Microsoft / GitHub default for many tiers)
- **Cursor** (selectable; "GPT-5.3" listed in their model lineup)
- ChatGPT desktop app with Code Interpreter
- Most legacy AI plugins (longest tail of integrations)

For full IDE landscape: [AI Coding Assistants 2026](/marcus_webb_87b5a26c/ai-coding-assistants-2026-cursor-vs-github-copilot-vs-claude-code-vs-cody-vs-continue).

## Side-by-side {#table}

| Aspect | Claude (Sonnet 4.6 / Opus 4.7) | GPT-5.5 |
|---|---|---|
| SWE-bench Verified | Opus 4.7: 87.6% | 88.7% (#1) |
| Latest release | Opus 4.7: April 16, 2026 | April 23, 2026 |
| Cheapest tier | Haiku 4.5 ($1/$5) | GPT-5-nano ($0.05/$0.40) |
| Mid tier | Sonnet 4.6 ($3/$15) | GPT-5.1 ($1.25/$10) |
| Top tier (input/output $/M) | Opus 4.7 ($5/$25) | GPT-5.5 ($5/$30) |
| Long context (standard) | Sonnet 4.6: 1M | GPT-5.5: 272K (then 2×/1.5×) |
| First-party CLI agent | Claude Code | None |
| Tool calling standard | MCP (open) | Native function-calling |
| Prompt caching | 90% discount | 90% discount |
| Batch discount | 50% | (per docs) |

## Pick by use case {#pick}

**Building agents or multi-step tools → Claude.** MCP, tool-call reliability, and Claude Code give it the edge.

**Tab completion / inline suggestions → either, depending on your IDE pick.** Cursor lets you switch; Copilot defaults to GPT-5.x with Claude Opus 4.6 on Pro+.

**Long-context / monorepo work → Sonnet 4.6 (1M).** The context advantage compounds and the price ($3/$15) is competitive.

**Bulk inference, cheapest credible model → GPT-5-nano ($0.05/$0.40)**. Half the cost of Haiku 4.5 input, eighth the output cost.

**Hardest SWE-bench-style problems → GPT-5.5** (88.7%). For the marginal 1.1 points over Opus 4.7, you pay roughly the same input rate and 20% more on output.

**Output-heavy agent loops → Opus 4.7 ($5/$25 vs GPT-5.5's $5/$30 output).** The output gap matters when the loop produces lots of code.

**Code review and PR analysis → Claude.** More disciplined plan-before-act behavior in practice.

**Python data science / Jupyter → GPT-5.x via ChatGPT Code Interpreter.** Still the best loop for one-off analysis work.

For prompt patterns that work well with both: [Prompt Engineering 2026](/aisha_kapoor_d69b3a75/prompt-engineering-2026-30-patterns-that-actually-work).

## FAQ {#faq}

### Should I use Claude or GPT-5.5 in Cursor?

Cursor lets you switch and lists Claude Sonnet 4.5, Claude Opus 4.6, GPT-5.3, Gemini 3 Pro, and its own **Composer model** (released October 2025, optimized for Cursor's agent loop, ~4× faster than similarly-capable models). The 2026 default for many users: Composer for fast inline edits, Claude or GPT-5.5 for harder tasks via Composer/Agent mode.

### Is Claude Opus 4.7 worth 5× the cost of Sonnet?

For routine coding: no. For multi-step reasoning, complex refactors, code review of large PRs: often yes. The Sonnet 4.6 → Opus 4.7 gap on SWE-bench is several points; the cost gap is 5×.

### What about Gemini 3 / DeepSeek V4?

**Gemini 3.1 Pro** scores 80.6% on SWE-bench Verified — solid but not at the top. **DeepSeek V4-Pro** (released April 24, 2026, open-weight, 1.6T MoE / 49B active, MIT license) scores 80.6% as well — the best open-weight option, though it requires datacenter hardware to self-host.

### Can I run Claude or GPT-5.5 locally?

No. Both are closed-weights. For local options that are competitive (not equal): [Best Local LLMs for Consumer Hardware (2026)](/jordan_lee_72db45ce/llama-3-3-vs-qwen3-vs-deepseek-r1-2026-local-llm-comparison-for-real-hardware).

### Which has fewer hallucinations on library names?

Anecdotally Claude in 2026 testing, but both still hallucinate. Always grep before trusting an API exists.

### Is there really a "Claude Mythos" model at 93.9%?

The SWE-bench Verified leaderboard listed "Claude Mythos Preview" at 93.9% in early 2026 reporting. As a preview model it isn't the public default; treat it as a research signal, not a production option.

## Bottom Line

The 2026 default stack:
- **Daily driver**: Claude Sonnet 4.6 ($3/$15, 1M context)
- **Hard problems**: Claude Opus 4.7 or GPT-5.5
- **Bulk inference / cost-sensitive**: GPT-5-nano or Claude Haiku 4.5
- **Top SWE-bench score on a single attempt**: GPT-5.5 (88.7%)

The differences narrowed in 2026. The choice is now driven by *workflow fit* — which IDE you live in, which CLI you trust, which standards you've built tooling around — not raw capability gaps.

## Sources {#sources}

- [Anthropic Pricing](https://platform.claude.com/docs/en/about-claude/pricing)
- [OpenAI API Pricing](https://openai.com/api/pricing/)
- [SWE-bench Leaderboards](https://www.swebench.com/)
- [Claude Opus 4.7 launch (April 16, 2026)](https://platform.claude.com/docs/en/about-claude/models/overview)
- [GPT-5.5 launch (April 23, 2026)](https://openai.com/index/introducing-gpt-5-5/)
- [Cursor Composer (October 2025)](https://cursor.com/changelog)
- [GitHub Copilot Plans](https://github.com/features/copilot/plans)
- MCP standard: modelcontextprotocol.io