LLM Reference

Claude Opus 4.7 vs GPT-5.3-Codex

Claude Opus 4.7 (2026) and GPT-5.3-Codex (2026) compare a standalone API model against a coding-specialized model. Claude Opus 4.7 ships a 1m-token context window, while GPT-5.3-Codex ships a 400k-token context window. On SWE-bench Verified, Claude Opus 4.7 leads by 2.6 pts. On pricing, GPT-5.3-Codex costs $1.75/1M input tokens versus $5/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.

Treat this as a product-type comparison: Claude Opus 4.7 is standalone API model, while GPT-5.3-Codex is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.

Decision scorecard

Local evidence first
SignalClaude Opus 4.7GPT-5.3-Codex
Product typeStandalone API modelCoding-specialized model
Best forreasoning-heavy apps, multimodal apps, and tool-calling agentscustom coding agents, code generation, and tool loops
Decision fitCoding, RAG, and AgentsCoding, RAG, and Agents
Context window1m400k
Cheapest output$25/1M tokens$14/1M tokens
Provider routes6 tracked3 tracked
Shared benchmarksSWE-bench Verified leader3 shared

Decision tradeoffs

Choose Claude Opus 4.7 when...
  • Claude Opus 4.7 holds a shared-benchmark lead on SWE-bench Verified, ahead by 2.6 points.
  • Claude Opus 4.7 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Claude Opus 4.7 has broader tracked provider coverage for fallback and route flexibility.
  • Claude Opus 4.7 uniquely exposes Multimodal in local model data.
  • Local decision data tags Claude Opus 4.7 for Coding, RAG, and Agents.
Choose GPT-5.3-Codex when...
  • GPT-5.3-Codex holds a shared-benchmark lead on Terminal-Bench 2.0, ahead by 7.9 points.
  • GPT-5.3-Codex has the lower cheapest tracked output price at $14/1M tokens.
  • GPT-5.3-Codex uniquely exposes Computer use in local model data.
  • Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Lower estimate GPT-5.3-Codex

Claude Opus 4.7

$10,250

Cheapest tracked route/tier: Anthropic

GPT-5.3-Codex

$4,900

Cheapest tracked route/tier: OpenRouter

Estimated monthly gap: $5,350. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.

Switch friction

Claude Opus 4.7 -> GPT-5.3-Codex
  • Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
  • GPT-5.3-Codex is $11/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Multimodal before moving production traffic.
  • GPT-5.3-Codex adds Computer use in local capability data.
GPT-5.3-Codex -> Claude Opus 4.7
  • Provider overlap exists on OpenRouter and Vercel AI Gateway; start route-level A/B tests there.
  • Claude Opus 4.7 is $11/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • Check replacement coverage for Computer use before moving production traffic.
  • Claude Opus 4.7 adds Multimodal in local capability data.

Specs

Specification
Released2026-04-162026-02-05
Context window1m400k
Parameters
ArchitectureDecoder OnlyDecoder Only
LicenseProprietaryProprietary
OpennessProprietaryProprietary
WeightsNot releasedNot released
CodeUnknownUnknown
Commercial useCommercial use: conditionalCommercial use: conditional
Knowledge cutoff2026-012025-08

Pricing and availability

Pricing attributeClaude Opus 4.7GPT-5.3-Codex
Input price$5/1M tokens$1.75/1M tokens
Output price$25/1M tokens$14/1M tokens
Providers

Capabilities

CapabilityClaude Opus 4.7GPT-5.3-Codex
VisionYesYes
MultimodalYesNo
ReasoningYesYes
JSON / Tool useYesYes
Structured outputsYesYes
Code executionYesYes
IDE integrationNoNo
Computer useNoYes
Parallel agentsNoNo

Benchmarks

BenchmarkClaude Opus 4.7GPT-5.3-Codex
SWE-bench Verified87.685.0
SWE-bench Pro64.356.8
Terminal-Bench 2.069.477.3

Continue comparing

Last reviewed: 2026-07-26. Data sourced from public model cards and provider documentation.