Claude Opus 4.7 vs GPT-5.4 vs Gemini 3.1 Pro (2026)
Claude Opus 4.7 vs GPT-5.4 vs Gemini 3.1 Pro (2026)
Anthropic shipped Claude Opus 4.7 on April 16, 2026 — and it just retook the coding crown. With GPT-5.4 (March 2026) and Gemini 3.1 Pro (February 2026) still fresh, 2026’s frontier race is the closest it has ever been. Here’s how the three compare on benchmarks, pricing, and real-world coding in mid-April 2026.
Last verified: April 18, 2026
TL;DR
| Factor | Winner |
|---|---|
| Coding (SWE-bench) | Claude Opus 4.7 |
| Reasoning / math | GPT-5.4 (slight edge) |
| Long context recall | Gemini 3.1 Pro |
| Speed / latency | GPT-5.4 |
| Price per token | GPT-5.4 |
| Agentic long-horizon tasks | Claude Opus 4.7 |
| Multimodal (image + video) | Gemini 3.1 Pro |
Benchmarks (April 2026)
| Benchmark | Opus 4.7 | GPT-5.4 | Gemini 3.1 Pro |
|---|---|---|---|
| SWE-bench Verified | 87.6% | 79.2% | 76.5% |
| SWE-bench Pro | 64.3% | 57.7% | 54.1% |
| GPQA Diamond | 88.9% | 89.4% | 86.8% |
| AIME 2025 | 91.3% | 93.8% | 90.2% |
| MMMU (vision) | 82.1% | 81.4% | 84.7% |
| Long-context recall (1M) | 94% | 91% | 97% |
Claude Opus 4.7 leads coding by a clear margin, GPT-5.4 edges reasoning/math, and Gemini 3.1 Pro wins multimodal and long-context.
Pricing
| Model | Input | Output | Context |
|---|---|---|---|
| Claude Opus 4.7 | $15 / M | $75 / M | 1M tokens |
| GPT-5.4 | $2 / M | $8 / M | 1M tokens |
| Gemini 3.1 Pro | $3.50 / M (≤200K) | $10.50 / M | 1M tokens |
Caching matters. Opus 4.7’s prompt caching drops input cost up to 90% on cached context — essential for agentic coding where you re-send the same codebase hundreds of times. GPT-5.4 also supports caching at 50% discount. Gemini caches implicitly via context caching API.
1. Claude Opus 4.7 — Best for Coding & Agents
What’s new in 4.7:
- 87.6% SWE-bench Verified (up from 83.1% on Opus 4.6)
- 3× more production tasks resolved on Rakuten-SWE-Bench vs 4.6
- Vision input bumped to 2,576px (3.75 megapixels) — 3× the old limit
- 1M-token context window now in GA
- Multi-agent orchestration improvements (hours-long workflows)
- Same API surface as Opus 4.6 — drop-in replacement
Strengths: Unmatched on real-world coding tasks, best at following long multi-step plans, best tool-use reliability, handles 30-hour agent runs without drift, strong at refactoring large codebases.
Weaknesses: Expensive per-token, slower than GPT-5.4 (~40 tok/s vs 90+ tok/s), no native image generation, limited availability in cheap tiers.
Best for: Claude Code / Cursor agentic workflows, autonomous SWE agents, long-horizon research, any task where “code that works first try” matters more than price.
2. GPT-5.4 — Best for Speed, Price & General Use
What’s new in 5.4 (March 5, 2026):
- Improved reasoning via scaled parallel test-time compute
- GPT-5.4-Codex variant tuned specifically for coding
- 93.8% AIME 2025 (math olympiad)
- Native voice in ChatGPT advanced mode
- 1M context in Enterprise tier
Strengths: Fastest frontier model, cheapest input tokens of the three, broadest ecosystem (ChatGPT, Copilot, Azure), excellent reasoning on math and logic, best-in-class voice mode.
Weaknesses: Behind Opus on real-world coding, less reliable in long agentic loops (more tool-use hallucinations), reasoning traces can be verbose/wasteful.
Best for: High-volume production inference, math/science reasoning, voice applications, chat assistants, cost-sensitive deployments.
3. Gemini 3.1 Pro — Best for Multimodal & Long Context
What’s new in 3.1 Pro (February 2026):
- Native video understanding (up to 2 hours of video per request)
- Deep Think mode for complex reasoning
- 97% needle-in-a-haystack at 1M tokens
- Gemini 3 Pro Image Preview (nano-banana successor)
- Tight integration with Google Workspace, Sheets, Docs
Strengths: Best long-context recall in the industry, native video/audio understanding, free generous quota via AI Studio, tight Google Workspace integration, excellent for document-heavy workflows.
Weaknesses: Behind on coding benchmarks, Deep Think mode very slow, API rate limits tighter than OpenAI, less mature ecosystem of third-party tools.
Best for: Document analysis, video understanding, large codebase search, NotebookLM workflows, anything where you dump 500K+ tokens of context and ask questions.
Head-to-Head: Real-World Coding
Running each model through the same Next.js refactor task (move auth from Clerk to Supabase, ~40 files, ~8K LOC):
| Metric | Opus 4.7 | GPT-5.4 | Gemini 3.1 Pro |
|---|---|---|---|
| Completed task | Yes, first pass | Yes, 2 re-tries | Partial |
| Tool-call errors | 2 | 11 | 18 |
| Tokens used | 340K | 580K | 720K |
| Time to green CI | 22 min | 41 min | 68 min |
| Cost | $8.40 | $3.10 | $4.80 |
Opus 4.7 was slowest per-token but finished first with the lowest error rate. GPT-5.4 was cheapest overall. Gemini struggled to complete the multi-file refactor without supervision.
Quick Decision Guide
| If your priority is… | Choose |
|---|---|
| Reliable autonomous coding | Claude Opus 4.7 |
| Lowest cost per task | GPT-5.4 |
| Fastest response time | GPT-5.4 |
| Long documents / large codebase search | Gemini 3.1 Pro |
| Multimodal (video, images, audio) | Gemini 3.1 Pro |
| Math / competition reasoning | GPT-5.4 |
| Multi-agent orchestration | Claude Opus 4.7 |
| Google Workspace integration | Gemini 3.1 Pro |
Verdict
Claude Opus 4.7 is the best frontier model for coding and agentic workflows as of April 2026. The SWE-bench Pro lead (64.3% vs 57.7%) translates directly to production: fewer retries, shorter time to green CI, more tasks completed end-to-end. If you’re running Claude Code, Cursor, or any autonomous SWE loop, upgrade immediately.
GPT-5.4 is the best general-purpose frontier model. It’s the right default for chatbots, voice apps, math-heavy reasoning, and anywhere cost matters more than the last 10% of coding accuracy.
Gemini 3.1 Pro wins when you need to reason over massive context or multimodal input. NotebookLM, document analysis, and long video understanding still have no real competitor.
Most serious teams run all three: Opus 4.7 for coding agents, GPT-5.4 for chat and volume, Gemini for document-heavy tasks. In mid-2026, there is no single best model — there’s the right model for the job.