Opus 4.7 leads on coding and context. GPT-5.4 is 7× cheaper. Here's the full breakdown on every benchmark that matters.
| Metric | Claude Opus 4.7 | GPT-5.4 | Note |
|---|---|---|---|
| SWE-bench Pro (coding) | 64.3%▲ | 57.7% | Opus +6.6 pts |
| SWE-bench Verified | 87.6%▲ | ~80% | Opus +7.6 pts |
| CursorBench (agents) | 70%▲ | 58% | Opus +12 pts |
| GPQA Diamond (reasoning) | 94.2% | 94.4% | Effectively tied |
| Vision accuracy | 98.5%▲ | Lower | Near-perfect on Opus |
| Vision resolution | 2,576px (~3.75MP)▲ | 2,048px | Opus handles larger images |
| Context window | 1,000,000 tokens▲ | 128,000 tokens | 7.8× more context |
| Max output | 32,000 tokens▲ | 16,384 tokens | 2× more output |
| Input pricing | $5.00 / 1M | $0.75 / 1M▲ | GPT is 6.7× cheaper |
| Output pricing | $25.00 / 1M | $4.50 / 1M▲ | GPT is 5.6× cheaper |
| Cached input | $0.50 / 1M | $0.19 / 1M▲ | Both offer caching |
When capability is more important than cost
When cost efficiency matters most
What 1 billion tokens per month costs on each model.
| Scenario | Claude Opus 4.7 | GPT-5.4 |
|---|---|---|
| 1B input tokens/mo (standard) | $5,000 | $750 |
| 1B input tokens/mo (with caching) | $500 | $190 |
| 1B output tokens/mo | $25,000 | $4,500 |
| Mixed 80/20 in/out (1B tokens) | $9,000 | $1,500 |
Opus 4.7 pricing also affected by new tokenizer — same text can use up to 1.35× more tokens vs Opus 4.6.
It depends on what you're doing. Opus 4.7 leads on coding (SWE-bench Pro: 64.3% vs 57.7%), vision accuracy (98.5% vs GPT-5.4's lower score), and context window (1M vs 128K tokens). GPT-5.4 is dramatically cheaper ($0.75/$4.50 vs $5/$25 per million tokens) and has comparable graduate-level reasoning (94.4% vs 94.2% GPQA Diamond — essentially a tie).
Claude Opus 4.7 wins on coding. SWE-bench Pro: 64.3% vs 57.7% — a meaningful 6.6-point gap. SWE-bench Verified: 87.6% vs ~80%. CursorBench: 70% vs 58%. If you're running coding agents, autonomous PR review, or complex software engineering workflows, Opus 4.7 is the better tool despite the higher price.
GPT-5.4 is significantly cheaper: $0.75/$4.50 per million tokens vs $5/$25 for Opus 4.7 — roughly 7× cheaper per token. However, Opus 4.7 with prompt caching ($0.50/1M input) can close the gap for cache-heavy workloads.
Claude Opus 4.7 by a wide margin: 1,000,000 tokens vs GPT-5.4's 128,000 tokens. If you need to process entire codebases, long legal documents, or large datasets in a single request, only Opus 4.7 can do it.
Claude Opus 4.7 has a near-perfect 98.5% vision accuracy and handles images up to 2,576px. GPT-5.4 is also strong on vision but lower accuracy. However, Gemini 3.1 Pro dominates video understanding (Video-MME: 78.2% vs the next best at 71.4%) — if video is your primary need, Gemini is the better pick.
Use Opus 4.7 if: coding quality is critical, you need 1M context, or you process high-resolution images. Use GPT-5.4 if: cost is the primary concern, you need general-purpose performance at scale, or you're building a cost-sensitive consumer product. For most high-volume apps where coding isn't the core feature, GPT-5.4 delivers better cost efficiency.
