GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro: A 2026 Cost-Per-Token Breakdown

In May 2026, three companies are racing for the frontier reasoning crown — and they've taken three very different pricing strategies. OpenAI's GPT-5.5 lists at $5 input / $30 output per 1M tokens. Anthropic's Claude Opus 4.7 lists at $5 / $25. Google's Gemini 3.1 Pro undercuts both at $2 / $12 for contexts under 200K.

Looks simple. It isn't. The right pick depends on your input-to-output ratio, your context-window needs, your tolerance for long-context surcharges, and how heavily you can lean on caching. This post walks through the math on four real workload shapes so you can pick the right one for yours.

The headline pricing (verified May 2026)

A few things jump out:

• GPT-5.5 and Opus 4.7 tie on input. They're literally the same price per input token. Whatever input-heavy workload you're modelling, you can swap them without changing the input line item.
• Opus 4.7 is ~17% cheaper on output. $25 vs $30 per 1M. Multiplied across millions of generated tokens, this matters more than it sounds.
• Gemini 3.1 Pro is the price leader. $2 / $12 is roughly 2.5× cheaper than the others on input and output — but only up to 200K context.
• Gemini's long-context cliff is real. Above 200K, you're paying $4 / $18 — still cheaper than Opus 4.7's flat $5 / $25, but the gap narrows from ~3× to ~1.4×.

Workload 1: Customer-facing chatbot (input-heavy, short outputs)

The default chatbot pattern is 2,000-token system prompt + history, with 200-token answers. Output ratio: 0.1.

Assumptions: 100,000 turns / month, 90% of the 2,000-token input cacheable (system prompt + few-shot examples).

Verdict: Gemini is 2.2× cheaper than Opus 4.7 and 2.5× cheaper than GPT-5.5. For a high-volume chatbot, Gemini 3.1 Pro is the clear winner — if quality clears your bar.

Workload 2: Code-generation agent (balanced input/output)

Coding agents have heavy input (codebase context, tool definitions, history) AND heavy output (generated code, explanations). Typical pattern: 8,000-token input, 2,000-token output. Output ratio: 0.25.

Assumptions: 10,000 generations / month, 60% input cache hit (system prompt, tool defs).

Verdict: Same ranking, but the gap narrows. Output-heavy workloads are where Opus 4.7's 17% output discount over GPT-5.5 starts mattering. Gemini still wins on raw cost, but for many coding benchmarks GPT-5.5 and Opus 4.7 measurably outperform Gemini — the choice becomes "is the 2.5× cost worth ~5% better correctness?"

Workload 3: Long-context document analysis (Gemini territory)

Single 500K-token input (a 1,000-page document), 5K-token analysis output. No caching benefit on single-shot. Run 100×/month.

Verdict: Gemini still wins, but the long-context surcharge eats most of its advantage. For documents over 200K, Gemini drops from 2.5× cheaper to 1.27× cheaper. Worth the choice if you need the 2M-token context window (none of the others can fit it); marginal otherwise.

Workload 4: High-stakes research (GPT-5.5 Pro vs Opus 4.7)

Multi-step research agent: 20K-token input (research plan + accumulated context), 8K-token output (detailed report). 1,000 reports / month.

Verdict: GPT-5.5 Pro is the most expensive of the four by an order of magnitude. It only makes sense for the very hardest workloads where its deliberative reasoning beats Opus 4.7 by a meaningful margin — and for most teams, that's a small slice of total volume. Route to GPT-5.5 Pro selectively, not by default.

The decision framework

After running these for hundreds of clients, the pattern that emerges is:

1. Default to Gemini 3.1 Pro if the workload is high-volume and quality-sensitive but not state-of-the-art-required. It's 2–3× cheaper, the context window is biggest, and the quality is genuinely competitive with GPT-5.5 and Opus 4.7 on most benchmarks.

2. Pick Claude Opus 4.7 when you need top-tier reasoning AND you have an output-heavy workload. The 17% output discount over GPT-5.5 compounds, and Opus's long-form coherence on multi-step tasks remains an edge.

3. Pick GPT-5.5 when you need top-tier reasoning AND your stack already has heavy OpenAI integration (tools, structured outputs, computer use) — the integration tax matters in real engineering hours.

4. Pick GPT-5.5 Pro sparingly, and only for the slice of workload that demonstrably needs it. It's a $30 / $180 model; route everything else away from it.

5. For everything that isn't reasoning-critical — chat, RAG, classification, JSON extraction — drop a tier to Sonnet 4.6, Gemini 3.5 Flash, or GPT-5 mini. The frontier reasoning models are usually overkill.

Run the math on your real usage

The numbers above are illustrative. Your actual workload will have its own ratio of input to output, its own cache hit rate, and its own context-length distribution. The calculator takes the same per-million-token rates and projects daily, monthly, and yearly spend across your real user count and message volume.

Further reading

• GPT-5.5 vs Claude Opus 4.7 — head-to-head cost comparison
• GPT-5.5 cost calculator
• Claude Opus 4.7 cost calculator
• Gemini 3.1 Pro cost calculator
• How to cut your AI API bill 70% in 2026