GPT-5 vs Claude Opus 4.7 (2026): Full Spec + Price + Use-Case Comparison

**GPT-5.5 lists at $5/1M input and $25/1M output. Claude Opus 4.7 lists at $15/1M input and $75/1M output.** Opus is 3x the input price and 3x the output price. That delta is not a small thing — for any workload that runs at scale, it's the dominant input to total cost of ownership, often more important than per-call quality differences.

**GPT-5.4** sits in between at $2.50/1M input and $15/1M output — half the GPT-5.5 price for ~95% of the quality on most tasks. Teams running production workloads where the marginal quality of 5.5 isn't worth 2x the cost typically default to 5.4. We see this split often: 5.5 for hard reasoning paths, 5.4 for the high-volume bread-and-butter calls.

**Caching changes the math materially.** Anthropic's 90% cache-read discount on Opus drops the effective input cost on cached prefixes from $15/1M to $1.50/1M — which makes Opus directly competitive with GPT-5.5 on workloads with long, repeated system prompts (RAG with stable instructions, agent harnesses with stable tool definitions). OpenAI's 50% prompt-cache hit discount on GPT-5.5 drops input to $2.50/1M on cache hits.

**Output is where Opus stays expensive.** No cache discount applies to output — and most agentic / coding workloads are output-heavy. A typical coding agent run that consumes 8K input and emits 4K output costs roughly $0.42 on GPT-5.5 vs $1.10 on Opus 4.7. At 10,000 runs/day, that's $4,200/day vs $11,000/day — a $2M/year delta.

**The right question is not 'which is cheaper'** — it's 'which closes the per-call quality gap enough to justify the output price difference at your actual call volume.' Use our Claude API cost calculator and OpenAI API cost calculator to plug your real input/output/cache-hit numbers in.

**GPT-5.5 and GPT-5.4 both expose a 400K-token input context window. Claude Opus 4.7 caps at 200K.** For most production workloads, both are more than enough — typical RAG calls land at 5-30K of context, and most coding workflows stay under 100K.

Where 400K matters: large codebase ingestion (loading 30-50 files into context for whole-system reasoning), long-document analysis (full 10-K filings, multi-hundred-page contracts, legal discovery sets), and meta-prompting (using one model to analyze the outputs of another over long traces).

Where 200K is fine: virtually all chat applications, agent harnesses with chunked retrieval, code-review-of-a-PR (diffs almost never exceed 100K), customer support workflows. The 200K limit becomes a constraint at the long tail — typically <5% of production calls — not in the median case.

**The output cap matters too.** GPT-5.5 will emit up to 128K output tokens in a single call; Opus 4.7 caps at 64K. For long-form generation (full document drafts, large code-file rewrites), GPT-5.5 has the practical edge — though for most agentic workloads you're emitting much less per call.

**Gemini 2.5 Pro is still the long-context king at 2M tokens** if context window is your binding constraint. We cover that comparison separately — see our GPT-4o vs Gemini 2.5 Pro guide for the long-context use case.

**SWE-bench Verified** (real-world software engineering, the most production-relevant benchmark in 2026): Claude Opus 4.7 lands at ~76%, GPT-5.5 at ~74%, GPT-5.4 at ~70%. Opus has held a small but consistent edge on this benchmark since the 4.0 series — Anthropic's RLHF and SFT pipeline is tuned specifically for coding agent workflows, and it shows.

**MMLU-Pro** (graduate-level multi-discipline reasoning): both flagship models are in the 88-90% range, with GPT-5.5 edging Opus 4.7 by 1-2 points on STEM-heavy subsets. For most production knowledge-work tasks the delta is inside the eval noise floor.

**GPQA Diamond** (PhD-level science questions, the hardest standardized reasoning eval): GPT-5.5 at ~71%, Opus 4.7 at ~70%. Effective parity. Both materially ahead of any 2025-era model.

**ARC-AGI** (abstract reasoning, the benchmark designed to resist memorization): GPT-5.5 with high reasoning effort takes this — ~58% vs Opus 4.7's ~52%. Worth noting: GPT-5.5 with reasoning effort cranked up consumes meaningfully more output tokens (and thus dollars) per call, which changes the price comparison. ARC-AGI doesn't translate directly to production workloads but it's a useful proxy for novel-problem reasoning.

**The honest takeaway**: on benchmark deltas alone, the two models are within 2-5 points of each other across the major evals. Opus wins SWE-bench. GPT-5.5 wins ARC-AGI and STEM-MMLU. Both win or lose by enough on different evals that benchmark-shopping won't settle the decision — the production behavior on YOUR workload will.

**Run your own eval.** Take 30 representative tasks from your production logs, run both models, blind-rate the outputs. Two days of work. Decides the question for your specific use case better than any leaderboard.

**Time-to-first-token (TTFT)** is what users feel. On a 4K-input prompt:

**GPT-5.5**: ~600-900ms p50 TTFT, ~1.5s p95. **GPT-5.4**: ~400-650ms p50, ~1.1s p95 (faster because less reasoning overhead). **Claude Opus 4.7**: ~700-1,000ms p50, ~1.8s p95. GPT-5.4 is the fastest of the three on first-token; the two top-of-line models are within 100-200ms of each other on TTFT.

**Sustained throughput** (tokens/sec after first token): GPT-5.5 sustains ~80-110 tok/s for plain text generation, Opus 4.7 sustains ~75-100 tok/s. Effective parity at the throughput level. Both meaningfully faster than the 2024-era flagship models (GPT-4o was ~50-70 tok/s, Opus 3.5 was ~50-65 tok/s).

**Streaming matters more than raw throughput.** Both APIs stream chunks reliably. Both support SSE. The user-perceived latency on a streaming chat UI is dominated by TTFT, not sustained throughput, so the 100-200ms difference is the one that matters for chat UX.

**Reasoning effort changes everything.** GPT-5.5 with `reasoning_effort: high` can take 30-90 seconds before emitting any user-visible output (it's internally generating reasoning tokens). Opus 4.7 with extended thinking mode similarly stretches into the 10-60 second range. For agent workloads where you can show a 'thinking...' indicator, this is fine. For chat UIs where the user expects immediate response, default to medium or low reasoning effort and reserve high for the hard paths.

**Regional latency varies.** OpenAI deploys across more global regions in 2026; Anthropic deploys via AWS Bedrock in addition to native API and has good US/EU/APAC coverage. If your users are concentrated in one region, test both from that region — TTFT differences of 100-300ms between providers are common.

**Both models accept image input natively** as part of the message API. Both handle the standard image formats (PNG, JPEG, WebP, GIF for first frame). Both have similar resolution caps (~2K longest side recommended for best results).

**Vision quality is roughly at parity** for the common tasks: chart/graph interpretation, document OCR, UI screenshot analysis, diagram understanding. Opus 4.7 has a slight edge on text-heavy images (multi-column documents, dense tables) in our internal evals — its OCR-via-vision pipeline tends to preserve structure better. GPT-5.5 edges out on natural images (photos, scenes) and on math/equation transcription.

**Vision input pricing**: both models bill image input as input tokens — typical cost is $5-20 per 1K images depending on resolution. Detailed math is in the OpenAI API cost calculator and Claude API cost calculator.

**Audio input**: GPT-5.5 supports audio input natively (audio tokens billed separately at ~$100/1M). Claude Opus 4.7 does not — Anthropic recommends transcribing to text first via a separate ASR pipeline. For voice-in workflows this is a real differentiator for GPT-5.5.

**Neither flagship outputs images or audio.** For image generation use GPT-Image-1, DALL-E 3, or a third-party model. For audio output use TTS APIs (OpenAI TTS, ElevenLabs).

**Both models support native function/tool calling** with parallel tool execution. The wire-format differs (OpenAI uses `tools[]` with function spec; Anthropic uses `tools[]` with tool spec — similar JSON schemas, slightly different field names) but the semantics are equivalent. Migration between them is a string-substitution exercise on tool definitions.

**Structured output** (forced JSON schema conformance) is where they diverge. **GPT-5.5 has strict mode** — pass `response_format: { type: 'json_schema', strict: true }` and OpenAI's API guarantees the output validates against your schema. This is a real differentiator: zero post-call validation failures, no retry loop needed.

**Claude Opus 4.7** coerces JSON via tool-use (define a single tool that wraps your desired output schema, force the model to call it). It works reliably but is one extra step in setup, and you handle the parsing on your side. Anthropic has signaled that strict JSON mode is in their roadmap but it's not GA as of June 2026.

**Parallel tool calling**: both support emitting multiple tool calls in a single response. GPT-5.5 is slightly more aggressive about parallelization in our testing (more willing to fan out 4-6 tools in one turn); Opus 4.7 tends to be more conservative (2-3 tools per turn typical).

**Tool-result tokens count as input** on both APIs — important for cost math on agent loops that pass large tool outputs back to the model. Cache the tool results if they're stable.

**Computer-use / browser-use tools**: Anthropic has the Claude Computer Use API (Opus 4.7 supported); OpenAI has equivalents via Assistants API and via GPT-5.5's tool ecosystem. Both are usable for agentic UI automation; neither is a finished product. Real production deployments are still rare.

**Anthropic's cache-read discount on Opus is 90%** — cached input tokens bill at $1.50/1M instead of $15/1M. The cache TTL is 5 minutes default (extendable to 1 hour with a flag, 1 hour billed at a premium write rate). Cache writes cost 25% more than uncached input.

**OpenAI's prompt-cache hit discount on GPT-5.5 is 50%** — cached input bills at $2.50/1M instead of $5/1M. The cache is automatic (no opt-in flag, no explicit cache-control markers). TTL is roughly 5-10 minutes depending on usage patterns.

**Math on a typical RAG workload**: 10K-token stable system prompt + tool defs + 2K-token user query + 1K-token output. Uncached on GPT-5.5: 12K × $5/1M + 1K × $25/1M = $0.085. Uncached on Opus 4.7: 12K × $15/1M + 1K × $75/1M = $0.255. **Cached** on GPT-5.5: 10K × $2.50/1M + 2K × $5/1M + 1K × $25/1M = $0.060. **Cached** on Opus 4.7: 10K × $1.50/1M + 2K × $15/1M + 1K × $75/1M = $0.120.

**The cache discount narrows the gap from 3x to 2x on cached prefixes** — material, but Opus is still meaningfully pricier on cached workloads.

**Caching only helps if your prompt prefix is actually stable.** If every call has a different system prompt (rare in well-designed apps) or you're constantly mutating the prefix (common in poorly-designed apps), neither cache fires and you pay full list. Audit your prompt construction for cache-friendliness before assuming the discount lands.

**Opus 4.8** (launched June 2026) inherits the same 90% cache-read discount. The Opus 4.7 vs 4.8 economic comparison is effectively flat — the differences are behavioral, not financial.

**Pick GPT-5.5 when**: your workload needs 400K context (large codebases, long documents), strict JSON mode (zero post-call validation failures), the cheapest frontier-tier model that still hits SWE-bench >70%, or audio input. Default for high-volume production where the marginal Opus quality isn't worth 3x output cost.

**Pick GPT-5.4 when**: GPT-5.5 quality is overkill for the task but you want OpenAI's tooling and ecosystem. The $2.50/$15 pricing is hard to beat for high-volume bread-and-butter calls — chat assistants, summarization pipelines, structured data extraction.

**Pick Claude Opus 4.7 when**: SWE-bench-style coding agents are the workload (the small edge compounds over agent turns), your prefix is highly cacheable (90% cache read closes the price gap to roughly 2x), behavioral stability matters more than newness (4.7 has been in production long enough to have predictable failure modes), or your team has standardized on Anthropic's API ergonomics and you don't want a second provider integration.

**Pick Opus 4.8 when**: you're starting a new project mid-2026 and want the latest behavior, you don't have an established eval suite that's tuned to 4.7's quirks, or you want the (small) quality bumps Anthropic shipped in the 4.8 release. For teams already in production on 4.7, the cost of re-validating eval suites against 4.8 usually outweighs the marginal quality gain.

**Hybrid is normal**: route the hard reasoning paths to Opus 4.7, route the high-volume routine calls to GPT-5.4 or GPT-5-mini. A well-built router can cut total spend 40-60% vs a monoculture on the flagship model. See our OpenAI → Claude migration tutorial for the multi-provider abstraction pattern.

**Profile**: 100,000 API calls/day. Average 5K input, 1.5K output per call. Stable 3K-token system prompt that caches.

**All-GPT-5.5, no cache**: 100K × (5K × $5 + 1.5K × $25) / 1M = 100K × $0.0625 = **$6,250/day = $2.28M/year**.

**All-GPT-5.5, 80% cache hit on the 3K prefix**: cached portion = 100K × 0.8 × 3K × $2.50/1M = $600/day. Uncached portion = 100K × (2K × $5 + 1.5K × $25) / 1M + 100K × 0.2 × 3K × $5/1M = $4,750 + $300 = $5,050/day. Total: **$5,650/day = $2.06M/year**.

**All-Claude-Opus-4.7, 80% cache hit on the 3K prefix**: cached portion = 100K × 0.8 × 3K × $1.50/1M = $360/day. Uncached portion = 100K × (2K × $15 + 1.5K × $75) / 1M + 100K × 0.2 × 3K × $15/1M = $14,250 + $900 = $15,150/day. Total: **$15,510/day = $5.66M/year**.

**Hybrid (70% GPT-5.4, 30% Opus 4.7, both cached)**: GPT-5.4 portion = 70K × ($0.0625 / 2 effective with cache) ≈ $1,800/day. Opus portion = 30K × $0.155 ≈ $4,650/day. Total: **$6,450/day = $2.35M/year**.

The all-Opus path costs **$3.6M/year more** than all-GPT-5.5. That's the price of the per-call quality edge at scale. Whether it's worth it depends entirely on whether your workload has the kind of quality bottleneck where Opus's SWE-bench edge translates into a material business outcome — fewer retries, fewer escalations, more first-shot-correct outputs.

**Run the numbers on your actual workload.** OpenAI API cost calculator and Claude API cost calculator take input/output/cache parameters and surface monthly + annual cost; cheaper than guessing wrong by 7 figures.

**Mistake 1: picking based on a benchmark leaderboard.** SWE-bench, MMLU, GPQA — they're useful directional signals, but a 2-5 point eval delta doesn't tell you which model will win on YOUR actual workload. Always run 30 representative tasks through both before committing.

**Mistake 2: ignoring caching in the price comparison.** Quoting list prices ($5 vs $15 input) without accounting for cache discounts overstates the GPT-5.5 cost advantage by 2x on cache-friendly workloads. Always compute effective price after cache.

**Mistake 3: pinning the flagship for high-volume routine calls.** Most production workloads have a long tail of easy calls (extraction, classification, summarization) that GPT-5.4 or even GPT-5-mini handles fine. Routing those off the flagship saves 60-80% of spend with negligible quality loss.

**Mistake 4: chasing the newest version reflexively.** Opus 4.8 just shipped. If you have a stable production deployment on 4.7 with a tuned eval suite, the cost of re-validating against 4.8 is usually higher than the marginal quality gain. Wait for a real reason to upgrade.

**Mistake 5: assuming model choice is binary.** The right answer is often hybrid — Opus 4.7 for the hard paths, GPT-5.4 for the easy paths, an explicit router that picks per call. We've seen 50%+ cost reductions from this pattern with no measurable quality loss.

**Mistake 6: ignoring prompt quality.** Whichever model you pin, the prompts you send it determine 60% of output quality. A weak prompt sent to Opus 4.7 will lose to a tight prompt sent to GPT-5.4 most days. Tighten your prompts before reaching for a more expensive model.

**OpenAI pricing**: openai.com/api/pricing/ and platform.openai.com/docs/models, fetched 2026-06-20. GPT-5.5 at $5/$25, GPT-5.4 at $2.50/$15, both with 400K context, both with 50% prompt-cache hit discount. Pricing has held steady since the GPT-5 line launched in early 2026.

**Anthropic pricing**: docs.anthropic.com/en/docs/about-claude/pricing, fetched 2026-06-20. Claude Opus 4.7 at $15/$75, Opus 4.8 at $15/$75 (cached input $1.50/1M), both with 200K context, both with the 90% cache-read discount. Pricing has held since the 4.x line launched.

**Benchmark numbers** (SWE-bench Verified, MMLU-Pro, GPQA Diamond, ARC-AGI): aggregated from each vendor's release notes and the public leaderboards (swebench.com, ARC Prize leaderboard). Where vendor-reported and independent numbers diverge, we cite the independent number.

**Latency numbers** (TTFT, sustained throughput): our internal monitoring across 50K production calls per model per week, May-June 2026, measured from us-east-1. Your numbers will vary by region and time-of-day.

**Live-verify before procurement**: pricing pages occasionally move. Check the source URLs above on the day you commit to a model choice. Cache discount mechanics also evolve — Anthropic moved from 5-minute-only to 5-min/1-hour optionality mid-2025, OpenAI's automatic caching threshold changed in late 2025.

**Eval methodology**: our SWE-bench numbers reflect the Verified subset (500 tasks, human-validated) run with the standard harness. ARC-AGI numbers are from the public test set, not the holdout. We don't run our own evals on MMLU-Pro or GPQA — those numbers come straight from vendor release notes.