GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro: Pricing Compared

The 2026 Frontier Model Landscape

Three models currently sit at the top of the AI coding stack: OpenAI's GPT-5.5, Anthropic's Claude Opus 4.7, and Google's Gemini 3.1 Pro. Each represents its provider's best reasoning, longest context, and highest code quality. But their pricing strategies differ sharply — and those differences compound fast across a real development project.

GPT-5.5 is OpenAI's flagship general-purpose model, positioned as the successor to the GPT-5.4 family with dramatically improved reasoning. Claude Opus 4.7 is Anthropic's most capable model, optimized for extended autonomous coding sessions. Gemini 3.1 Pro is Google's latest premium offering, combining strong quality with a million-token context window. All three are viable for large codebase work.

Raw Pricing Comparison

Here are the current API prices for each model, per million tokens:

At first glance, GPT-5.5 and Claude Opus 4.7 share the same input price ($5.00/M), but Opus is 17% cheaper on output ($25 vs $30). Gemini 3.1 Pro undercuts both by 60% on input and 52-60% on output. GPT-5.5 Pro exists for specialized deep-reasoning tasks — at $30/$180 it is 6x the price of standard GPT-5.5 and not practical for general coding workflows.

Cost-per-Task Benchmarks for Coding

Raw token prices mean nothing without context. Here are three common coding tasks with estimated token usage, based on real-world agent sessions. We assume an average of 65,000 input tokens and 800 output tokens per turn (typical for CLI coding agents reading context each turn).

Task 1: Generate a New Module (~50 turns)

Building a complete feature module from scratch — e.g., a payments integration with webhook handling, database models, and API routes. Estimated: 3.25M input tokens, 40K output tokens.

• GPT-5.5: (3.25 x $5.00) + (0.04 x $30.00) = $16.25 + $1.20 = $17.45
• Claude Opus 4.7: (3.25 x $5.00) + (0.04 x $25.00) = $16.25 + $1.00 = $17.25
• Gemini 3.1 Pro: (3.25 x $2.00) + (0.04 x $12.00) = $6.50 + $0.48 = $6.98

Task 2: Refactor a 500-Line File (~20 turns)

Breaking apart a monolithic file, extracting utilities, improving types, and fixing logic. Estimated: 1.3M input tokens, 16K output tokens.

• GPT-5.5: (1.3 x $5.00) + (0.016 x $30.00) = $6.50 + $0.48 = $6.98
• Claude Opus 4.7: (1.3 x $5.00) + (0.016 x $25.00) = $6.50 + $0.40 = $6.90
• Gemini 3.1 Pro: (1.3 x $2.00) + (0.016 x $12.00) = $2.60 + $0.19 = $2.79

Task 3: Write a Test Suite (~30 turns)

Generating comprehensive unit and integration tests for an existing module. Estimated: 1.95M input tokens, 24K output tokens.

• GPT-5.5: (1.95 x $5.00) + (0.024 x $30.00) = $9.75 + $0.72 = $10.47
• Claude Opus 4.7: (1.95 x $5.00) + (0.024 x $25.00) = $9.75 + $0.60 = $10.35
• Gemini 3.1 Pro: (1.95 x $2.00) + (0.024 x $12.00) = $3.90 + $0.29 = $4.19

Across all three tasks combined, the total spend is: GPT-5.5: $34.90, Claude Opus 4.7: $34.50, Gemini 3.1 Pro: $13.96. Gemini costs roughly 60% less than the other two for identical token volumes.

When the Expensive Model Is Actually Cheaper

Token price is only half the equation. The other half is how many tokens you burn to get working code. A model that nails the implementation on the first pass costs less than a cheap model that needs three attempts — even if its per-token rate is higher.

Consider the module generation task. If Gemini 3.1 Pro needs 1.5x the turns to reach the same quality (75 turns instead of 50 due to retries and corrections), its actual cost becomes:

• Gemini 3.1 Pro adjusted: (4.875M x $2.00) + (0.06M x $12.00) = $9.75 + $0.72 = $10.47
• Claude Opus 4.7 (first-pass): $17.25

In this scenario, Gemini is still cheaper even with 50% more turns. But what if it needs 3x the turns (150 turns) on a complex task with subtle logic bugs? Then: (9.75M x $2.00) + (0.12M x $12.00) = $19.50 + $1.44 = $20.94 — now more expensive than Opus, and you spent 3x the wall-clock time debugging.

A practical quality-adjusted cost framework: multiply the raw cost by a retry factor. Based on community benchmarks for complex coding tasks in May 2026:

• Claude Opus 4.7: retry factor ~1.1x (rarely needs correction on well-specified tasks)
• GPT-5.5: retry factor ~1.2x (occasionally verbose or misses edge cases)
• Gemini 3.1 Pro: retry factor ~1.4x (handles most tasks well, struggles on complex multi-file logic)

Applying these to the combined three-task total: Opus: $34.50 x 1.1 = $37.95, GPT-5.5: $34.90 x 1.2 = $41.88, Gemini: $13.96 x 1.4 = $19.54. Even quality-adjusted, Gemini 3.1 Pro remains the cheapest option for most workflows. The premium models justify their price primarily on developer time saved — fewer debugging cycles, less manual intervention.

How to Choose

Here is a decision framework based on the numbers above:

• Budget-constrained, high volume: Gemini 3.1 Pro. At $2.00/$12.00 per million, you get flagship-tier reasoning at mid-range pricing. Best for prototyping, test generation, and projects where you can tolerate some retries.
• Maximum code quality, minimal babysitting: Claude Opus 4.7. The $5/$25 pricing is steep, but the 1.1x retry factor means you pay once and move on. Ideal for complex multi-file refactors and production-critical code.
• Balanced middle ground: GPT-5.5. Same input price as Opus, slightly higher output cost, but strong general reasoning. Works well if you are already in the OpenAI ecosystem.
• Avoid for general coding: GPT-5.5 Pro. At $30/$180, it is designed for specialized deep-reasoning tasks (mathematical proofs, complex research) — not everyday software development.

One more consideration: prompt caching. Both Anthropic and OpenAI offer caching that can reduce input costs by 80-90% on repeated context. If you are running a 50+ turn coding session where the agent re-reads the same files each turn, Opus with caching can drop from $17.25 to roughly $4.73 per module generation — putting it below Gemini uncached.

Want to run the exact numbers for your project? Use the AI Cost Estimator to plug in your codebase size, feature count, and preferred tooling. It calculates costs across all three models (and 60+ others) so you can make the decision with real data, not guesswork.