Claude Opus 4.6 vs GPT-5 Nano

Summary of head-to-head results (scores are our 1–5 internal tests unless noted):

Wins for Claude Opus 4.6 (A):

• strategic_analysis: A 5 vs B 4 — Claude tied for 1st ("tied for 1st with 25 other models out of 54 tested"). This means better nuanced tradeoff reasoning and number-driven decisions in our tasks.
• creative_problem_solving: A 5 vs B 3 — Claude tied for 1st ("tied for 1st with 7 other models out of 54 tested"). Expect more non-obvious, actionable ideas in brainstorms.
• agentic_planning: A 5 vs B 4 — Claude tied for 1st ("tied for 1st with 14 other models out of 54 tested"). Better decomposition, failure recovery, and multi-step plans in our agent tests.
• tool_calling: A 5 vs B 4 — Claude tied for 1st ("tied for 1st with 16 other models out of 54 tested"). In our tests Claude selected functions and arguments more accurately and sequenced calls more reliably.
• faithfulness: A 5 vs B 4 — Claude tied for 1st ("tied for 1st with 32 other models out of 55 tested"). Fewer hallucinations and tighter adherence to source content in our tasks.
• safety_calibration: A 5 vs B 4 — Claude tied for 1st ("tied for 1st with 4 other models out of 55 tested"). Claude refused disallowed prompts more consistently while allowing legitimate requests.
• persona_consistency: A 5 vs B 4 — Claude tied for 1st ("tied for 1st with 36 other models out of 53 tested"). Stronger character maintenance and injection resistance.

Wins for GPT-5 Nano (B):

• structured_output: B 5 vs A 4 — GPT-5 Nano tied for 1st ("tied for 1st with 24 other models out of 54 tested"). In our JSON/schema adherence tasks GPT-5 Nano produced cleaner, more schema-compliant outputs.

Ties (no clear winner in our tests):

• constrained_rewriting: both 3 (rank 31 of 53), classification: both 3 (rank 31 of 53), long_context: both 5 (both tied for 1st with many models), multilingual: both 5 (tied for 1st). Long-context parity (A context_window 1,000,000 vs B 400,000) shows both handle 30k+ retrieval tasks in our suite, though Opus offers a larger raw context window.

External benchmarks (Epoch AI):

• SWE-bench Verified: Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI), ranking 1 of 12 (sole holder) — supporting Opus’s coding strength in our judge tasks.
• math_level_5: GPT-5 Nano scores 95.2% on math_level_5 (Epoch AI), ranking 7 of 14.
• AIME 2025: Claude Opus 4.6 scores 94.4% vs GPT-5 Nano 81.1% on AIME 2025 (Epoch AI); Opus ranks 4 of 23 while Nano ranks 14 of 23.

What this means for real tasks: Claude Opus 4.6 is meaningfully stronger for multi-step agent workflows, safety-critical content, code-level tasks (supported by SWE-bench), and high-fidelity reasoning. GPT-5 Nano is the better pick when you need rigid structured outputs, math contest strength on specific external tests, and extreme cost/latency efficiency.

Costs per 1,000 tokens (mtok) are: Claude Opus 4.6 input $5 and output $25; GPT-5 Nano input $0.05 and output $0.40. At scale (input+output): • 1M tokens/month = Claude $30,000 (51000 + 251000), GPT-5 Nano $450 (0.051000 + 0.41000). • 10M tokens/month = Claude $300,000, GPT-5 Nano $4,500. • 100M tokens/month = Claude $3,000,000, GPT-5 Nano $45,000. The output-cost ratio is 62.5× (25/0.4). Developers of high-volume chat, analytics, or consumer-facing apps should care about the gap—GPT-5 Nano turns multi‑million token budgets into practical deployments, while Claude Opus 4.6 is only affordable where its higher capabilities justify the cost.

Choose Claude Opus 4.6 if you need: • production agent workflows, multi-step planning, and reliable tool calling; • highest faithfulness and safety calibration; • top SWE-bench Verified coding performance (78.7% on Epoch AI) and strong AIME (94.4%). Accept the much higher price ($25 per 1k output tokens) for those gains. Choose GPT-5 Nano if you need: • the lowest cost at scale (total ≈ $450 per 1M tokens vs $30,000 for Opus), • best-in-class structured output/schema adherence, or • a fast, low-latency developer tool where budget and throughput matter more than peak agentic reasoning.