GPT-5.5 vs Claude Opus 4.7: Cost & Performance Comparison (2026)

The two flagship reasoning models in May 2026 are GPT-5.5 (OpenAI) and Claude Opus 4.7 (Anthropic). Both are top-tier. Both are expensive. Both are great at different things — and the answer to "which should I use" is much less obvious than the marketing pages suggest.

This article breaks down the head-to-head in three dimensions:

1. Pricing math — how the costs compare across realistic workloads
2. Capability strengths — where each model wins
3. Decision framework — practical rules for which to pick when

Quick summary

Dimension	GPT-5.5	Claude Opus 4.7
Input price	$5.00 / 1M	$15.00 / 1M
Output price	$20.00 / 1M	$75.00 / 1M
Cached input	$1.25 / 1M (75% off)	$1.50 / 1M (90% off)
Batch input	$2.50 / 1M	$7.50 / 1M
Batch output	$10.00 / 1M	$37.50 / 1M
Context window	256K tokens	1M tokens
Max output	32K tokens	16K tokens
Vision	✓	✓
Audio	✓	—
Tool use	Strong	Best-in-class

Headline pricing: Claude Opus 4.7 is 3.5× more expensive per call than GPT-5.5 at face value.

With caching applied: the gap can collapse to under 2× or even invert in Opus's favor for specific workloads. Read on.

Per-call cost across scenarios

For a single API call, varying token mix:

Scenario	Input / Output	GPT-5.5	Opus 4.7	Opus / GPT ratio
Short chat	100 / 50	$0.0015	$0.0053	3.5×
Standard chat	1,000 / 500	$0.0150	$0.0525	3.5×
Long context Q&A	50,000 / 500	$0.260	$0.788	3.0×
Code generation	500 / 2,000	$0.0425	$0.158	3.7×
RAG with retrieval	10,000 / 300	$0.056	$0.173	3.1×

At face value, GPT-5.5 wins on cost across every scenario by ~3×. So why is anyone using Opus?

What changes the math: caching

Anthropic's prompt caching is the most aggressive in the industry — cached read at $1.50 per 1M tokens, exactly the same as GPT-5.5's standard input price.

Real-world example: a code review agent.

System prompt + tool spec = 5,000 tokens (stable across calls). User message + retrieved code = 1,000 tokens (varies per call). Output = 800 tokens. Volume: 1,000 calls/day, 95% cache hit rate after warmup.

GPT-5.5 (with caching):

Daily input cost = (5,000 × 1,000 × 0.95 / 1M) × $1.25 cached  → $5.94
                 + (5,000 × 1,000 × 0.05 / 1M) × $5.00 standard → $1.25
                 + (1,000 × 1,000 / 1M) × $5.00 standard        → $5.00
Daily output cost = (800 × 1,000 / 1M) × $20.00                  → $16.00
Total/day = $28.19

Claude Opus 4.7 (with caching):

Daily input cost = (5,000 × 1,000 × 0.95 / 1M) × $1.50 cached  → $7.13
                 + (5,000 × 1,000 × 0.05 / 1M) × $15.00 standard → $3.75
                 + (1,000 × 1,000 / 1M) × $15.00 standard        → $15.00
Daily output cost = (800 × 1,000 / 1M) × $75.00                  → $60.00
Total/day = $85.88

With caching: Opus is 3× more expensive than GPT-5.5 — output cost still dominates. So caching doesn't fully close the gap when output is heavy.

But for input-heavy, output-light workloads like classification or extraction (1,000 calls/day, 5K cached input + 1K user input + 100 output):

GPT-5.5  daily total = $5.94 + $1.25 + $5.00 + ($0.10 × 1000 × $20/1M) = $14.19
Opus 4.7 daily total = $7.13 + $3.75 + $15.00 + ($0.10 × 1000 × $75/1M) = $33.38

Still 2.4× more, but tighter. The takeaway: caching narrows the gap but doesn't eliminate it. Output cost is the killer.

When Opus wins anyway

Cost is rarely the only consideration. Opus 4.7 is ~3× more expensive but wins on quality for:

1. Hard reasoning chains

When a task requires 5-10 sequential reasoning steps without errors propagating, Opus's chain-of-thought integrity is consistently stronger than GPT-5.5. Examples:

• Multi-file code refactoring with cross-file invariants
• Legal document analysis with conditional clauses
• Math/proof generation
• Complex SQL query construction with multi-table joins

In production agent loops where errors compound, a 3× more expensive model that's 30% more reliable is a net win because retries and human escalations are expensive.

2. Long-context cleanly

Opus 4.7's 1M token context is 4× larger than GPT-5.5's 256K. More importantly, Anthropic has invested heavily in long-context attention quality — the model genuinely uses information from token 800,000 vs token 50,000 effectively. GPT-5.5 starts to forget mid-context for very long inputs.

For book-length analysis, whole-codebase review, or large-document QA, this matters more than the cost gap.

3. Tool use reliability

Both models support tool/function calling well. Opus's tool-use behavior is more consistent:

• Fewer hallucinated function calls
• Better adherence to JSON schemas
• More reliable when given many tools (10+)
• Stronger at deciding not to call a tool when unnecessary

For agent frameworks where one wrong tool call can corrupt state, this reliability is genuinely worth more than the cost premium.

4. Coding quality

Anthropic has consistently led on coding benchmarks since Claude 3.5. Opus 4.7 maintains the lead — generating cleaner code, with fewer logical bugs, better adherence to existing project conventions, and stronger refactoring instincts.

For coding-specific products (Cursor, Windsurf, Cline, etc.), Opus is often the default despite the cost.

When GPT-5.5 wins

Equally significant — there are many workloads where GPT-5.5 is the better choice even ignoring cost:

1. Multimodal pipelines

GPT-5.5 has native audio + vision in a single model. Opus 4.7 has vision but no audio (separate model needed). For workflows that span text, image, and audio, GPT-5.5 is simpler.

2. Tooling ecosystem

Most LLM frameworks default to OpenAI compatibility. Switching to Anthropic adds friction:

• Different SDK
• Slightly different tool-calling format
• Different streaming protocols
• Different content moderation behavior

For prototypes or MVPs, GPT-5.5 is the path of least resistance.

3. Rate limits and reliability

OpenAI has historically had higher rate limits per tier and more mature infrastructure. For consumer-facing applications with traffic spikes, this matters.

4. Output volume tasks

If you're generating long outputs (essays, reports, code at scale), GPT-5.5's $20/1M output vs Opus's $75/1M is a 3.75× cost gap that no caching can close. For these workloads, Opus is structurally more expensive.

5. Cost-sensitive everyday tasks

For tasks that don't require frontier reasoning — summarization, classification, basic Q&A, content moderation — the 3× cost gap to Opus is hard to justify. GPT-5 mini or Gemini 3.0 Flash beats both for these tasks anyway.

A practical decision framework

When in doubt:

Is the task hard reasoning where errors compound?
  └─ Yes → Opus 4.7
  └─ No → next question

Is context > 200K tokens?
  └─ Yes → Opus 4.7 (or Gemini 3.0 Pro for 2M)
  └─ No → next question

Is output token volume high (>1K per call)?
  └─ Yes → GPT-5.5 (3.75× cheaper output)
  └─ No → next question

Are you committed to the OpenAI ecosystem?
  └─ Yes → GPT-5.5
  └─ No → next question

Are you doing a coding agent product?
  └─ Yes → Opus 4.7 (best coding quality)
  └─ No → GPT-5.5 (default safe choice)

A pattern that works: tiered routing

Sophisticated production setups don't pick one model — they route based on task difficulty:

User request arrives
  ↓
Classifier (cheap GPT-5 mini call)
  ↓
"Simple chat / FAQ"     → GPT-5 mini       ($0.0006 / call)
"Code or reasoning"     → o4-mini           ($0.0027 / call)
"Hard reasoning"        → Claude Opus 4.7   ($0.0525 / call)
"Long context analysis" → Gemini 3.0 Pro    ($0.0075 / call)

This pattern gets you 80% of the quality at 20% of the cost vs sending everything to Opus.

Cost projections at scale

For the same workload (10K calls/day, 1K input + 500 output average, with caching at 80%):

Model	Cost per 1K calls	Monthly (300K calls)
GPT-5 mini	$0.45	$135
GPT-5.5	$11.25	$3,375
Claude Opus 4.7	$36.75	$11,025

The Opus monthly bill is 3× higher than GPT-5.5, 80× higher than GPT-5 mini. Whether that's worth it depends entirely on what fraction of your queries actually need frontier reasoning.

What we recommend

After all the math:

• Default: route between GPT-5 mini (cheap path) and GPT-5.5 (frontier path). Covers 90% of use cases at reasonable cost.
• Add Opus 4.7 for specific high-stakes tasks: agent loops, hard coding, long-context analysis. Use the routing pattern above.
• Don't use Opus as a general-purpose model. The cost gap doesn't justify it.
• Always enable caching for both models — it's free money.

Try the math yourself

Use the calculator on the homepage — load both GPT-5.5 and Opus 4.7 in the comparison table, plug in your actual token counts, toggle caching, and see the real numbers for your workload.

For a head-to-head spec sheet:

• GPT-5.5 cost calculator →
• Claude Opus 4.7 cost calculator →

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Pricing reflects rates verified May 2026. Capability claims are based on public benchmark performance and developer reports as of writing. Both providers update models frequently — verify current state when committing.