GPT-5.5 vs Claude Opus 4.7: pricing, context, and where each one wins

Two frontier models shipped within a week of each other in April 2026: Anthropic released Claude Opus 4.7 on April 16, and OpenAI followed with GPT-5.5on April 23. Both target long-horizon agentic coding and both ship a 1M-token context window. Pick the wrong one and your monthly bill can swing 2–6× on the same workload. This post is a factual, engineer-to-engineer guide to choosing between them — sourced directly from OpenAI and Anthropic docs, not vibes.

1. Who is newer, who is older

Both models were released in April 2026 and both replace earlier flagship Opus / GPT-5.x SKUs. The week of overlap matters: when you read “benchmarks vs the previous generation” in either company’s blog post, the comparison point is roughly two weeks of calendar age.

• Claude Opus 4.7 — released 2026-04-16. API model id claude-opus-4-7. Anthropic positions it as “our most capable generally available model for complex reasoning and agentic coding.”
• GPT-5.5 — released 2026-04-23. API model id gpt-5.5 (snapshot gpt-5.5-2026-04-23). OpenAI positions it as a “new class of intelligence for coding and professional work,” with a separate gpt-5.5-pro SKU targeting the highest end.

Both replace prior flagships from late 2025 / early 2026 (Opus 4.6 and GPT-5.4, respectively). Both keep the same model families alive: at OpenAI you can still call gpt-5.4 and the Codex variants; at Anthropic claude-opus-4-6 stays in rotation at the same price.

2. Pricing, side by side

This is the section most engineers care about. All numbers are per 1M tokens, in USD, taken from the official pricing pages cited at the bottom of this article.

Standard API pricing

• GPT-5.5: $5 input, $0.50 cached input, $30 output.
• GPT-5.5 Pro: $30 input, $180 output. (No cached-input discount listed.)
• Claude Opus 4.7: $5 input, $25 output, $0.50 per cache read, $6.25 per 5-minute cache write, $10 per 1-hour cache write.

Two things to read into this. First, input parity: GPT-5.5 and Opus 4.7 both charge $5 per million input tokens, with a 90%-discounted cache hit rate at $0.50. Second, output divergence: GPT-5.5 charges $30 per million output tokens versus Opus 4.7’s $25 — a ~17% price gap that compounds quickly on verbose tasks (long code refactors, multi-step plans, agent traces).

The hidden tokenizer cost on Opus 4.7

Anthropic explicitly warns in the migration notes that Opus 4.7 ships with a new tokenizer that may emit up to 35% more tokensfor the same input text. The multiplier ranges from 1.0× to 1.35× depending on content type, and code / structured data tend to hit the upper end. So if you migrate an existing Opus 4.6 workload, your per-request cost can rise by up to 35% even though the per-token rate is unchanged.

Practical implication: when you back-of-envelope a switch from GPT-5.5 to Opus 4.7, do not assume the $5 / $25 list price gives Opus a clean 17% output discount — the tokenizer can erase most of that on text-heavy or code-heavy traffic.

Long-context billing

• GPT-5.5: prompts over 272K input tokens incur a 2× input and 1.5× output surcharge for the session.
• Claude Opus 4.7: 1M context at standard pricing with no long-context premium— a 900K-token request is billed at the same per-token rate as a 9K-token one.

If your workload regularly exceeds ~270K input tokens (large codebase analysis, log triage, big PDFs), Opus 4.7’s flat long-context pricing is a meaningful structural advantage.

Batch / Flex / Priority modifiers

• OpenAI offers Batch and Flexat 50% off ($2.50 input / $15 output for GPT-5.5), and Priorityat 2.5× ($12.50 input / $75 output).
• Anthropic offers Batchat 50% off ($2.50 input / $12.50 output for Opus 4.7).

A concrete cost-estimate example

Take a typical agentic-coding session: ~50K input tokens (system prompt plus file context), ~15K output tokens, run 1,000 times. Plain math:

• GPT-5.5: input 50,000 × 1,000 × $5/M = $250; output 15,000 × 1,000 × $30/M = $450; total ~$700.
• Claude Opus 4.7(ignoring tokenizer inflation): $250 + 15,000 × 1,000 × $25/M = $250 + $375 = $625.
• Claude Opus 4.7(assuming a 1.3× tokenizer multiplier on code): $325 input + $487 output = ~$812.

In this comparison Opus 4.7 looks ~10% cheaper on bare list price — but once you account for realistic tokenizer inflation it ends up ~15% more expensivethan GPT-5.5. That’s exactly why list price alone is not a decision input: you have to dry-run on your own traffic. The OminiGate usage dashboard lets you compare actual token cost of the same prompt across different slugs, which is the simplest way to settle this.

3. Context, output cap, and reasoning

Both models advertise a 1M-token context window, but the operating envelopes differ.

• GPT-5.5 — 1,050,000 max input tokens, 128K max output. Knowledge cutoff December 1, 2025. (Codex still uses a 400K context.)
• Claude Opus 4.7 — 1M context, 128K max output via the synchronous Messages API, up to 300K via the Batch API with a beta header. Reliable knowledge cutoff Jan 2026.

Reasoning controls are where the two diverge most. GPT-5.5 keeps OpenAI’s reasoning_effort parameter (low / medium / high). Claude Opus 4.7 introduces an adaptive-thinking-only regime: setting thinking: {type: "enabled", budget_tokens: N} now returns a 400 error. You instead pick an effort level — low / medium / high / xhigh / max— and Claude self-budgets its thinking tokens. Anthropic recommends starting at xhigh for coding and agentic work.

Two more Opus 4.7 changes that often surprise migrating users: sampling parameters (temperature, top_p, top_k) are rejected with 400 if set to non-default values, and thinking content is omitted by default from responses. If your product streams reasoning to users, you must opt in with display: "summarized" or you will see a long pause before output begins.

4. Where each one wins (with numbers)

Cross-referencing OpenAI’s GPT-5.5 launch numbers and Anthropic’s Opus 4.7 page (plus AWS’s Bedrock launch post), the picture that emerges is “both lead, on different axes.”

Coding benchmarks

• SWE-bench Verified — Opus 4.7: 87.6%. GPT-5.5 was not scored on this benchmark in the launch materials.
• SWE-bench Pro — Opus 4.7: 64.3%(up from 53.4% on Opus 4.6). GPT-5.5: 58.6%.
• Terminal-Bench 2.0 — GPT-5.5: 82.7%. Opus 4.7: 69.4%.
• CursorBench — Cursor reported Opus 4.7 at 70%versus Opus 4.6 at 58%; GPT-5.5 was not reported on this benchmark.

Reasoning, math, knowledge

• GPQA Diamond — GPT-5.5: 93.6%. Opus 4.7: 94.2%. Effectively a tie.
• FrontierMath Tier 1–3 — GPT-5.5: 51.7%. (Opus 4.7 not directly comparable in published materials.)
• ARC-AGI-1 — GPT-5.5: 95.0%.
• Humanity’s Last Exam — GPT-5.5: 52.2%; GPT-5.5 Pro: 57.2%.

Tool use and agents

• Toolathlon — GPT-5.5: 55.6%; Opus 4.7: 54.6%. Tie.
• tau2-bench Telecom — GPT-5.5: 98.0%.
• OSWorld-Verified (computer use) — GPT-5.5: 78.7%.
• Finance Agent v1.1 — Opus 4.7: 64.4%.

The honest summary, as a working engineer: Opus 4.7 is the stronger pick for resolving issues inside an existing codebase (SWE-bench-style work) and for long, multi-hour agentic sessions. GPT-5.5 is the stronger pick for terminal-driven planning and execution, computer use, and broad knowledge tasks. The pure-reasoning gap (GPQA, MMLU-style) is small enough that price and integration ergonomics should drive the choice.

5. API differences you will actually hit

Reasoning / thinking

OpenAI uses a single string parameter:

{
  "model": "gpt-5.5",
  "reasoning_effort": "high",
  "messages": [...]
}

Anthropic Opus 4.7 uses an output-config block, with effort and an optional advisory task budget:

{
  "model": "claude-opus-4-7",
  "max_tokens": 64000,
  "thinking": { "type": "adaptive" },
  "output_config": {
    "effort": "xhigh",
    "task_budget": { "type": "tokens", "total": 128000 }
  },
  "messages": [...]
}

Prompt caching

• OpenAIcaches prefixes automatically — you pay $0.50 / 1M for hits, no explicit markers needed.
• Anthropic requires explicit cache_control on content blocks (or a single top-level breakpoint). Cache hits are 0.1× base input price; 5-minute writes are 1.25×, 1-hour writes are 2×.

Sampling and determinism

On Opus 4.7, setting temperature, top_p, or top_k to non-default values returns a 400. Migrate by deleting these fields and shaping output through prompts. GPT-5.5 still accepts the standard sampling knobs.

6. Calling both through OminiGate

OminiGate exposes both providers under one API key (sk-omg-…) so you can A/B test or fall over between them without juggling two billing accounts. Two endpoint flavors:

• OpenAI-compatible: https://api.ominigate.ai/v1
• Anthropic-compatible: https://api.ominigate.ai

GPT-5.5 via the OpenAI SDK

from openai import OpenAI

client = OpenAI(
    api_key="sk-omg-...",
    base_url="https://api.ominigate.ai/v1",
)

resp = client.chat.completions.create(
    model="openai/gpt-5.5",
    reasoning_effort="high",
    messages=[
        {"role": "user", "content": "Refactor this Go service to use pgx instead of GORM."},
    ],
)
print(resp.choices[0].message.content)

Claude Opus 4.7 via the Anthropic SDK

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-omg-...",
    base_url="https://api.ominigate.ai",
)

msg = client.messages.create(
    model="anthropic/claude-opus-4.7",
    max_tokens=64000,
    thinking={"type": "adaptive"},
    output_config={"effort": "xhigh"},
    messages=[
        {"role": "user", "content": "Refactor this Go service to use pgx instead of GORM."},
    ],
)
print(msg.content[0].text)

Switching between them at request time

Because both are exposed through the same gateway, an OpenAI-compatible client can also call Anthropic models if your gateway routes by slug. This snippet is the simplest way to A/B test both heads behind a feature flag without changing infrastructure:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OMINIGATE_KEY,
  baseURL: "https://api.ominigate.ai/v1",
});

const slug = useOpus ? "anthropic/claude-opus-4.7" : "openai/gpt-5.5";

const completion = await client.chat.completions.create({
  model: slug,
  messages: [{ role: "user", content: prompt }],
});

Note: each OminiGate endpoint is single-modality — chat goes to the chat endpoint, image generation has its own endpoint, video generation has its own. You cannot stuff a Sora-style request into the chat endpoint.

7. How to choose

A short decision tree based on the data above:

1. You are resolving issues in a real codebase (large repo, edits across files, multi-hour Cursor / Cline / Claude Code sessions) → Opus 4.7 at effort xhigh. SWE-bench Verified 87.6% and flat 1M context pricing both favor it.
2. You are running an autonomous agent that drives a terminal or browser → GPT-5.5. 82.7% on Terminal-Bench 2.0 and 78.7% on OSWorld-Verified are concrete leads.
3. Your prompts regularly exceed 270K input tokens (long log analysis, large PDF QA, big-repo retrieval) → Opus 4.7. The 2× surcharge GPT-5.5 levies past 272K is significant.
4. You need rich tool-use with sampling control (temperature, structured outputs with controlled variance) → GPT-5.5. Opus 4.7 rejects non-default sampling params.
5. You want the absolute frontier on a hard, one-off problem → GPT-5.5 Pro at $30 / $180, or Opus 4.7 at maxeffort. Expect single-call cost to be 3–6× the standard SKU.
6. You are doing high-volume background work (classification, summarization at scale) → pick whichever you already integrate with and use Batch (50% off on both). The model gap matters less than the discount.

And of course: route through OminiGate and you don’t have to commit. Switch model per request and let production traffic decide.

Sources

• What’s new in Claude Opus 4.7 — Claude API Docs — model id claude-opus-4-7, 1M context, 128K max output, adaptive-thinking-only, sampling-params 400, new tokenizer (up to 1.35×), xhigh effort, task budgets beta.
• Anthropic Claude pricing page — Opus 4.7 $5 / $25 base, $0.50 cache hit, $6.25 / $10 cache writes, batch $2.50 / $12.50, 1M context at standard pricing.
• Anthropic models overview — Opus 4.7 reliable knowledge cutoff Jan 2026, 128K max output, 300K via Batch API beta header.
• Anthropic effort parameter docs — effort levels including xhigh exclusive to Opus 4.7 and recommended starting points.
• Introducing Claude Opus 4.7 — release April 16, 2026; SWE-bench Pro 64.3%, CursorBench 70%, Rakuten-SWE-Bench 3× Opus 4.6.
• AWS Bedrock launch post for Claude Opus 4.7 — SWE-bench Verified 87.6%, Terminal-Bench 2.0 69.4%, Finance Agent v1.1 64.4%.
• GPT-5.5 model docs — OpenAI — gpt-5.5-2026-04-23 snapshot, 1,050K input, 128K output, knowledge cutoff Dec 1 2025, 272K long-context surcharge.
• OpenAI pricing page — GPT-5.5 $5 / $0.50 / $30; GPT-5.5 Pro $30 / $180; Batch and Flex 50% off; Priority 2.5×.
• GPT-5.5 complete guide — Digital Applied — Terminal-Bench 2.0 82.7%, SWE-Bench Pro 58.6%, OSWorld-Verified 78.7%, GPQA Diamond 93.6%, FrontierMath, ARC-AGI-1, Humanity’s Last Exam, Toolathlon 55.6%.
• GPT-5.5 release guide — ofox.ai — cross-check on SWE-Bench Pro (GPT-5.5 58.6% vs Opus 4.7 64.3%), Terminal-Bench (82.7% vs 75.1%).
• Claude Opus 4.7 pricing breakdown — CloudZero — tokenizer multiplier 1.0×–1.35×, batch / cache mechanics.
• OminiGate model catalog — verified live slugs openai/gpt-5.5, openai/gpt-5.5-pro, anthropic/claude-opus-4.7.