Anthropic Claude Sonnet 4 vs GPT 5.4

Anthropic Claude Sonnet 4 (OpenRouter, 1,000,000-token context) versus GPT 5.4 (Azure AI Foundry, 1,050,000-token context). GPT 5.4 is cheaper by 3% on a blended token mix. GPT 5.4 uniquely supports parallel tool calls and pdf input. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.

Bottom line — Anthropic Claude Sonnet 4 vs GPT 5.4

Anthropic Claude Sonnet 4 and GPT 5.4 are priced within 3% of each other, so cost alone is not the deciding factor. The comparison comes down to capabilities, context window, and benchmark performance on the specific task shape your workload demands.

On capability surface area, the models diverge: GPT 5.4 supports parallel tool calls where the other does not; GPT 5.4 supports pdf input where the other does not; GPT 5.4 supports structured output (json schema) where the other does not. These differences are binary — either your workload needs the capability or it does not. Check whether any critical path in your agent pipeline depends on a capability only one model provides before committing to a migration.

For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.

Side-by-side cost

Live workload comparison

Same workload run through both models. The cheaper one is highlighted.

3,000
01,050,000
400
0128,000
5,000
01,000,000
OpenRouter
$2,283/mo
Input $3.00/M · Output $15.00/M
GPT 5.4Cheaper
Azure AI Foundry
$2,055/mo
Input $2.50/M · Output $15.00/M
At this workload, GPT 5.4 is 10% cheaper than Anthropic Claude Sonnet 4 — a savings of $228/month ($2,739/year).
Production recipe — Agent Command Center
strategy: cost-optimized
primary:
  model: gpt-5-4
  provider: azure-ai-foundry
fallback:
  model: anthropic-claude-sonnet-4
  provider: openrouter
shadow: { sample_rate: 0.05 }   # mirror 5% of traffic to compare quality live
Anthropic Claude Sonnet 4 GPT 5.4
Input price $3.00/M $2.50/M
Output price $15.00/M $15.00/M
Context window 1,000,000 1,050,000
Max output 64,000 128,000
Function calling
Vision
Audio input
Reasoning
Prompt caching
Structured output
Pricing verified Aug 6, 2026 Aug 6, 2026
Cheaper option
~3% cheaper than the priciest in this pair
Larger context
1,050,000 tokens
More capabilities
5 of 6 capability flags advertised

Benchmark comparison

Side-by-side public benchmark scores. Greener bar = winner.

Chatbot Arena ELOgeneral
Anthropic Claude Sonnet 4
GPT 5.4
1,477
ARC-AGIreasoning
Anthropic Claude Sonnet 4
GPT 5.4
93.7%
GPQA Diamondreasoning
Anthropic Claude Sonnet 4
GPT 5.4
92.8%
MMMU-Promultimodal
Anthropic Claude Sonnet 4
GPT 5.4
81.2%
ARC-AGI-2reasoning
Anthropic Claude Sonnet 4
GPT 5.4
73.3%
SWE-benchagent
Anthropic Claude Sonnet 4
GPT 5.4
57.7%
FrontierMathmath
Anthropic Claude Sonnet 4
GPT 5.4
47.6%
Humanity's Last Examreasoning
Anthropic Claude Sonnet 4
GPT 5.4
39.8%

Cost at scale: monthly spend at three usage volumes

Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.

Scale Anthropic Claude Sonnet 4 GPT 5.4 Delta
Startup
10K requests/day
$1,800 /mo $1,650 /mo $150/mo
Mid-market
100K requests/day
$18,000 /mo $16,500 /mo $1,500/mo
Enterprise
1M requests/day
$180,000 /mo $165,000 /mo $15,000/mo

At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.

Capability diff — what you gain and lose on the swap

A specific list of what each model has that the other doesn't. If your workload depends on a row in Only Anthropic Claude Sonnet 4, switching to GPT 5.4 means re-architecting that path (and vice versa).

Only on Anthropic Claude Sonnet 4
Nothing — everything Anthropic Claude Sonnet 4 ships is also on GPT 5.4.
Only on GPT 5.4
  • • Parallel tool calls
  • • PDF input
  • • Structured output (JSON schema)
Capabilities both share (5)
  • ✓ Function calling
  • ✓ Vision input
  • ✓ Streaming
  • ✓ Prompt caching
  • ✓ Native reasoning mode

Migration considerations

Concrete differences to wire through your stack before you flip traffic from one to the other.

  • Max output tokens differ: 64,000 on Anthropic Claude Sonnet 4 vs 128,000 on GPT 5.4. Long-form generation tasks may truncate differently — adjust streaming UI and chunking accordingly.
  • GPT 5.4 has capabilities Anthropic Claude Sonnet 4 lacks: Parallel tool calls, PDF input, Structured output (JSON schema). Worth wiring through the agent design before commit.
  • Provider changes from OpenRouter to Azure AI Foundry. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.

How to A/B test Anthropic Claude Sonnet 4 vs GPT 5.4 in production

If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:

  1. 1. Point your existing OpenAI SDK at https://gateway.futureagi.com/v1. No code change beyond base_url and a virtual key.
  2. 2. Mark Anthropic Claude Sonnet 4 primary, mirror 20% of traffic to GPT 5.4 in shadow mode. Both responses are logged; only the primary is served to users.
  3. 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
  4. 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.

Full walkthrough on the Agent Command Center page.

FAQ — Anthropic Claude Sonnet 4 vs GPT 5.4

Which is cheaper, Anthropic Claude Sonnet 4 or GPT 5.4?

GPT 5.4 is cheaper by roughly 3% on a blended input + output token mix. Input prices are $3.00/M for Anthropic Claude Sonnet 4 versus $2.50/M for GPT 5.4; output prices are $15.00/M versus $15.00/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.

What is the context window of Anthropic Claude Sonnet 4 versus GPT 5.4?

Anthropic Claude Sonnet 4 supports up to 1,000,000 tokens of context. GPT 5.4 supports up to 1,050,000 tokens. GPT 5.4 has the larger window by a factor of 1.1x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.

Do Anthropic Claude Sonnet 4 and GPT 5.4 both support tool calling?

Yes — both Anthropic Claude Sonnet 4 and GPT 5.4 support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.

Which model supports prompt caching for cost reduction?

Both Anthropic Claude Sonnet 4 and GPT 5.4 support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.

How do I A/B test Anthropic Claude Sonnet 4 against GPT 5.4 in production?

Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.