Claude Sonnet 4.6 vs GPT-5.4 Nano

Overview (our internal testing unless noted): Sonnet wins 6 tests, GPT-5.4 Nano wins 2, and 4 tests tie. Breakdown by test (score A = Claude Sonnet 4.6, score B = GPT-5.4 Nano; rankings use the provided display strings):

• Tool calling: Sonnet 5 vs Nano 4. Sonnet is tied for 1st with 16 others out of 54 tested ("tied for 1st with 16 other models out of 54 tested"). This matters for agent pipelines: Sonnet is more likely in our tests to pick the right function, order calls, and supply correct arguments.
• Safety calibration: Sonnet 5 vs Nano 3. Sonnet is tied for 1st with 4 others out of 55 ("tied for 1st with 4 other models out of 55 tested"). For compliance-sensitive apps, Sonnet more reliably refuses harmful requests while permitting legitimate ones.
• Faithfulness: Sonnet 5 vs Nano 4. Sonnet is tied for 1st with 32 others ("tied for 1st with 32 other models out of 55 tested"); Nano ranks 34 of 55. For tasks that require sticking to source material, Sonnet produced fewer hallucinations in our tests.
• Agentic planning: Sonnet 5 vs Nano 4. Sonnet tied for 1st ("tied for 1st with 14 other models out of 54 tested"); Nano ranks 16 of 54. Sonnet scored better on goal decomposition and recovery strategies.
• Classification: Sonnet 4 vs Nano 3. Sonnet is tied for 1st with 29 others ("tied for 1st with 29 other models out of 53 tested"); Nano ranks 31 of 53. For routing and categorization, Sonnet was more accurate in our suite.
• Creative problem solving: Sonnet 5 vs Nano 4. Sonnet tied for 1st with 7 others ("tied for 1st with 7 other models out of 54 tested"); Nano ranks 9 of 54. Sonnet generated more non-obvious, feasible ideas in our prompts.
• Structured output: Sonnet 4 vs Nano 5. Nano is tied for 1st with 24 others out of 54 ("tied for 1st with 24 other models out of 54 tested"). In JSON/schema tasks, Nano better adhered to strict formats in our tests.
• Constrained rewriting: Sonnet 3 vs Nano 4. Nano ranks 6 of 53 ("rank 6 of 53 (25 models share this score)"); Sonnet ranks 31 of 53. For tight character/byte-limited rewrites, Nano compressed content more reliably.
• Strategic analysis: tie 5 vs 5. Both tied for 1st (Sonnet display: "tied for 1st with 25 other models out of 54 tested"). Both handle nuanced tradeoffs with numbers equally well in our testing.
• Long context: tie 5 vs 5. Both tied for 1st with 36 others ("tied for 1st with 36 other models out of 55 tested"). Both preserved retrieval accuracy with 30K+ token contexts in our suite.
• Persona consistency: tie 5 vs 5. Both tied for 1st with 36 others ("tied for 1st with 36 other models out of 53 tested").
• Multilingual: tie 5 vs 5. Both tied for 1st with 34 others ("tied for 1st with 34 other models out of 55 tested").

External benchmarks (Epoch AI): Sonnet scores 75.2 on SWE-bench Verified ("rank 4 of 12 (sole holder)") — cited as SWE-bench Verified (Epoch AI). On AIME 2025 (Epoch AI) Sonnet scores 85.8 (rank 10 of 23) vs GPT-5.4 Nano 87.8 (rank 8 of 23); cite: AIME 2025 (Epoch AI). Use-case interpretation: Sonnet shows stronger agentic/tool, safety, and faithfulness signals in our internal suite; GPT-5.4 Nano is measurably better at strict structured outputs and compact rewriting, and it edges Sonnet on the AIME 2025 external math score by 2.0 percentage points.

Rates from the payload are per mTok (per 1,000 tokens). Cost per 1,000 tokens: Claude Sonnet 4.6 input $3, output $15; GPT-5.4 Nano input $0.20, output $1.25. Per 1,000,000 tokens (1,000 mToks): Claude Sonnet 4.6 = $3,000 (1M input) or $15,000 (1M output); GPT-5.4 Nano = $200 (1M input) or $1,250 (1M output). Example mixed scenario (50% input / 50% output) per month: Sonnet = $9,000 per 1M tokens, $90,000 per 10M, $900,000 per 100M; GPT-5.4 Nano = $725 per 1M, $7,250 per 10M, $72,500 per 100M. If you expect sustained multi-million token volumes (10M–100M+/mo), the GPT-5.4 Nano price saves hundreds of thousands to millions of dollars; teams building cost-sensitive, high-throughput APIs or consumer-facing apps should care most. If accuracy, agentic planning, or safety calibration materially reduce downstream costs (e.g., fewer human reviews, fewer incidents), Sonnet’s higher fees may be justified despite the 12× price ratio.

Choose Claude Sonnet 4.6 if: you need top-tier tool calling, safety calibration, faithfulness, agentic planning, or creative problem solving for high-value workflows (e.g., automated agents, legal/medical drafting review, complex codebase automation). Sonnet won 6 of 12 internal tests and tied for 1st in several high-impact categories. Choose GPT-5.4 Nano if: you operate at high token volumes or need a lightweight, low-latency engine for structured outputs, constrained rewriting, or consumer-scale services where cost per token is critical. GPT-5.4 Nano wins structured_output and constrained_rewriting in our tests and costs roughly $1,250 per 1M output tokens vs Sonnet’s $15,000 per 1M.