Claude Sonnet 4.6 vs GPT-5.4: Same Price, Different Wins

OpenAI released GPT-5.4 on March 5, 2026 and priced it at $2.50/$15 per million tokens. Anthropic's Claude Sonnet 4.6, out since February 17, runs $3.00/$15. On output - where most cost builds up in production - they're identical. On input, Anthropic costs 20% more.

For that 20% input premium, you get a model that's 2-3x faster, doesn't surcharge you for long contexts, and beats GPT-5.4 on SWE-bench. GPT-5.4 counters with stronger autonomous agent behavior, higher GPQA Diamond scores, and native computer use that's better tested in the wild. Neither wins everything, and the gap on most benchmarks is close enough to matter less than your specific workload.

Quick Comparison

Claude Sonnet 4.6: What Anthropic Got Right

Claude Sonnet 4.6 closes to within two points of Claude Opus 4.6 on coding and computer use benchmarks, at one-fifth the price. That positioning is the whole story. Anthropic built a model that makes Opus-class results accessible without Opus-class cost.

The 1M token context window is the headline feature, and the flat-rate pricing matters more than the number. GPT-5.4 technically supports 1M tokens too, but prompts past 272K trigger a 2x input surcharge for the entire session. For a codebase analysis at 400K tokens, Sonnet costs $1.20 in input; GPT-5.4 runs $5.00. At scale, that arithmetic compounds quickly.

Speed is the other structural advantage. Sonnet 4.6 creates 44-63 tokens per second; GPT-5.4 produces 20-25. In an IDE assistant or streaming API response, users perceive this difference. In batch pipelines running thousands of completions, it cuts wall-clock time in half.

On SWE-bench Verified - the coding benchmark that best predicts real-world agent performance on software engineering tasks - Sonnet 4.6 scores 79.6%, two points above GPT-5.4's 77.2%. It also scores 72.5% on OSWorld, close to GPT-5.4's 75.0%. For instruction-following in creative and writing tasks, third-party evaluations consistently rank Claude models above GPT-5.x when given complex multi-constraint briefs.

Batch pricing at $1.50/$7.50 per MTok makes Sonnet 4.6 compelling for high-volume async workloads: test generation, linting, documentation passes over large repos.

Claude Sonnet 4.6: Strengths

• Faster output (2-3x vs GPT-5.4)
• 1M context without pricing cliff at 272K
• Stronger SWE-bench (79.6% vs 77.2%)
• Better instruction-following on complex multi-constraint prompts
• More recent knowledge cutoff (Aug 2025)
• 64K max output window for long-form generation

Claude Sonnet 4.6: Weaknesses

• GPQA Diamond 74.1% vs GPT-5.4's 92.8% - significant gap on hard science reasoning
• Computer use requires tool integration, not native
• Web search not built in - needs external tool calls
• $0.50/MTok higher input cost than GPT-5.4

GPT-5.4: OpenAI's Unified Reasoning Play

GPT-5.4, released March 5, 2026, merged OpenAI's coding and reasoning lineages into a single model. Prior generations required choosing between GPT-5.x for general tasks and separate reasoning-focused variants. GPT-5.4 handles both, with native computer use as the operational headline.

The computer use capability is genuinely better-tested in deployment than Anthropic's toolchain-based approach. GPT-5.4 scores 75.0% on OSWorld-Verified, above the 72.4% human expert baseline and Claude Sonnet 4.6's 72.5%. For workflows involving desktop automation, browser navigation, or spreadsheet manipulation, GPT-5.4 is the more reliable choice in production.

The GPQA Diamond gap deserves attention. Sonnet 4.6 scores 74.1%; GPT-5.4 scores 92.8%. GPQA Diamond tests graduate-level physics, chemistry, and biology reasoning on problems designed to resist surface-level pattern matching. If your use case involves scientific literature analysis, hypothesis evaluation, or cross-disciplinary reasoning, the 18-point gap matters in practice.

Terminal-Bench 2.0 shows a similar story for autonomous coding agents: GPT-5.4 at 75.1% vs Sonnet 4.6 at 59.1%. When models are tasked with autonomous terminal sessions - installing dependencies, debugging build failures, iterating without human guidance - GPT-5.4 shows more reliable execution. This tracks with the model's architecture; OpenAI explicitly optimized GPT-5.4 for agentic autonomy.

The native web search integration is also practical. Sonnet 4.6 can call web search as a tool, but GPT-5.4 has it built into the reasoning loop with better latency. For agents that need current documentation, API references, or real-time data mid-task, GPT-5.4 avoids a round-trip.

GPT-5.4: Strengths

• GPQA Diamond 92.8% - far ahead on hard science reasoning
• Better autonomous terminal agents (75.1% vs 59.1% Terminal-Bench)
• Native computer use (75.0% OSWorld)
• Native web search in the reasoning loop
• 128K max output for large-context generation
• $0.50/MTok lower input cost

GPT-5.4: Weaknesses

• 2x input surcharge past 272K tokens - expensive for long-context workloads
• 20-25 tokens/sec output - noticeably slower in interactive use
• SWE-bench Verified 77.2% trails Sonnet's 79.6% on standard coding tasks
• Earlier training knowledge cutoff than Sonnet 4.6

Benchmark Comparison

The pattern is consistent: Sonnet 4.6 leads on standard coding tasks and productivity work; GPT-5.4 leads on hard reasoning, autonomous agents, and computer use. The only true tie is basic HumanEval-style code generation, where both models saturate the benchmark.

Pricing Analysis

Both models are priced closer than they look at first glance. Output tokens dominate costs in real production workloads (responses are normally 3-10x longer than prompts), and both models bill output at $15/MTok.

The long context math is where Sonnet's input premium inverts. At 400K input tokens:

• Sonnet 4.6: 400K × $3.00 = $1.20
• GPT-5.4: 400K × $5.00 (2x surcharge applied to full session) = $2.00

At 600K tokens, the gap widens further. Any team running regular long-context workloads - full codebase reviews, long document analysis, extended agent sessions - will find Sonnet cheaper despite the higher base rate.

For short-context, high-frequency workloads (queries under 10K tokens), GPT-5.4 is modestly cheaper: roughly 17% less on a 3:1 input-output ratio.

Prompt caching is cheaper with Sonnet ($0.30 vs $0.625 per MTok for cached reads). If your application reuses system prompts or large document contexts across many calls, that 2x difference in cache hit pricing compounds fast.

The existing GPT-5.4 vs Claude Opus 4.6 comparison covered the flagship tier. This matchup is different because Sonnet 4.6 and GPT-5.4 land close enough on output pricing that the decision really comes down to workload characteristics, not budget tier.

Verdict

For most developers and teams, Claude Sonnet 4.6 is the better default. The speed advantage is material in interactive contexts, the flat-rate 1M context window removes budget surprises for long-form workloads, and SWE-bench performance is slightly better for everyday coding tasks. Anthropic's instruction-following on complex multi-constraint prompts is an underrated strength for document processing and enterprise automation.

GPT-5.4 earns the router slot for specific task types. Hard science reasoning, autonomous terminal sessions, and computer use workflows consistently go to GPT-5.4. If your application needs an agent that can navigate a desktop, install software, or reason through graduate-level research papers without human hand-holding, GPT-5.4 handles those tasks more reliably.

Choose Claude Sonnet 4.6 if:

• Your workload frequently tops 100K tokens per request
• You run high-throughput pipelines where response latency matters
• You're building coding assistants or IDE integrations
• Writing quality and instruction-following precision are core to your use case

Choose GPT-5.4 if:

• You're building autonomous computer-use agents
• Scientific or technical reasoning is a core use case
• You need an AI that can run terminal sessions independently
• Web search is needed in the reasoning loop without extra integration work

Choose either if:

• Basic coding tasks under HumanEval difficulty are your primary workload
• Your prompts are consistently under 100K tokens
• You're already vendor-locked through Azure (GPT-5.4) or AWS Bedrock (Sonnet 4.6)

The smart play for cost-sensitive teams is to route to Sonnet 4.6 by default, then escalate to GPT-5.4 when hitting specific capability walls. Third-party benchmarks suggest this hybrid cuts costs by 40-60% versus exclusive GPT-5.4 usage with minimal quality loss on standard tasks. The Claude Code vs Cursor vs Codex comparison covers the tooling layer above these models if you're evaluating IDE-level integrations rather than raw API access.

Sources:

• Claude Sonnet 4.6 model profile - Awesome Agents
• Claude Sonnet 4.6 benchmarks and pricing - NxCode
• Claude API official pricing - Anthropic
• GPT-5.4 review and benchmarks - BuildFastWithAI
• GPT-5.4 complete guide - NxCode
• Claude Sonnet 4.6 vs GPT-5.4 coding comparison - NxCode
• Sonnet 4.6 vs GPT-5.4 model comparison - Artificial Analysis
• GPT-5.4 vs Claude Sonnet 4.6 comparison - Viblo/CometAPI
• GPT-5.4 pricing on OpenRouter