GPT-4o vs Claude 3.5 Sonnet — API Cost Calculator

Predict your real monthly bill. Toggle batch API and prompt caching to see how 50% discounts and cache hits change the math for your exact workload.

Cost Calculator

Input tokens per request

Output tokens per request

Requests per month

Use Batch API (50% off, up to 24h latency) Use prompt caching

⬛ GPT-4o OpenAI

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per month

🟠 Claude 3.5 Sonnet Anthropic

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per month

Pricing snapshot (as of May 2026)

The table below shows the per-1M-token rates sourced directly from the official OpenAI and Anthropic pricing pages, last verified on 21 May 2026. All figures are in USD.

Rate type	GPT-4o	Claude 3.5 Sonnet
Input (standard)	$2.50	$3.00
Output (standard)	$10.00	$15.00
Input (batch)	$1.25	$1.50
Output (batch)	$5.00	$7.50
Cache write	—	$3.75
Cache read	$1.25	$0.30

Sources: platform.openai.com/docs/pricing · anthropic.com/pricing. OpenAI does not charge a separate cache-write fee; caching is applied automatically.

When GPT-4o is the better pick

GPT-4o is the stronger choice when your application relies on multimodal inputs. It natively accepts images, audio, and video frames in the same API call, whereas Claude 3.5 Sonnet handles vision but lacks native audio ingestion. If you are building a product that processes uploaded receipts, interprets user-submitted photos, or transcribes spoken input as part of a single model call, GPT-4o is the only option between these two.

The OpenAI ecosystem is also broader on structured outputs. GPT-4o supports a JSON schema mode that constrains the model to emit syntactically valid JSON matching a user-supplied schema — useful for tool-calling pipelines, form extraction, and data transformation where downstream parsers expect strict structure. While Claude supports tool use and can be prompted to emit JSON reliably, it does not enforce schema compliance at the API layer.

Multimodal inputs — vision, audio, and structured image analysis in a single call.
Azure deployment — GPT-4o is available via Azure OpenAI Service with data-residency options in the EU and US, which matters for GDPR-sensitive enterprise workloads.
Lower base input cost — $2.50/1M versus $3.00/1M. At pure input-heavy workloads (e.g., long document summarization with short answers), GPT-4o is 17% cheaper on input and 33% cheaper on output before any optimizations.
Ecosystem tooling — OpenAI's Assistants API, fine-tuning support, and broader third-party SDK coverage give GPT-4o more integration surface area today.

When Claude 3.5 Sonnet is the better pick

Claude 3.5 Sonnet consistently scores at or near the top of agentic coding benchmarks. On SWE-bench Verified, Anthropic reported Claude 3.5 Sonnet resolving over 49% of real-world GitHub issues — a meaningful lead over GPT-4o on autonomous software engineering tasks. If your product involves multi-step code generation, debugging loops, or tool-calling agents that navigate codebases, Claude 3.5 Sonnet is worth the higher list price.

The more compelling cost story emerges when you use prompt caching aggressively. Claude's cache read price is $0.30 per million tokens — just 10% of the base $3.00 input rate. Consider a customer-support bot with a 50,000-token knowledge-base system prompt that is reused 10,000 times per day. Without caching, that system prompt alone costs $1,500 per day in input tokens. With Claude caching — one cache write plus 9,999 cache reads — the effective cost drops to roughly $150 per day: a 90% reduction from that single optimization. No other model in this tier offers cache reads that cheap.

Long-context reasoning — 200K context window with strong in-context retrieval quality, versus GPT-4o's 128K.
Agentic coding — best-in-class on SWE-bench; preferred by developers using Cursor, Cline, and similar AI coding tools.
Prompt caching ROI — cache reads at $0.30/1M make large reusable system prompts dramatically cheaper than on any competing model.
No hallucinated JSON keys — Claude's instruction-following in XML-tagged prompts tends to produce cleaner structured outputs without explicit schema enforcement.

Batch API: the most underused 50% discount

Both OpenAI and Anthropic offer a batch processing API that cuts input and output costs by 50% in exchange for relaxed latency — up to 24 hours for results. This discount is uniform across both providers and applies to the same models, making it purely a workload-scheduling decision rather than a model-selection one.

The batch API is well-suited for workloads that can tolerate overnight processing: nightly analytics pipelines that summarize the previous day's user activity, content moderation queues that do not need real-time decisions, data labeling jobs where human reviewers work the next morning, and embedding generation for documents added to a knowledge base during business hours. The worst use case is anything customer-facing that requires a response in under a few seconds — interactive chat, streaming completions, and real-time tool calls are categorically incompatible with batch.

At 1M requests per month with 2,000 input tokens and 500 output tokens per request, enabling batch API saves $2,500 per month on GPT-4o and $3,375 per month on Claude 3.5 Sonnet compared to standard pricing. That is meaningful recurring savings for workloads that genuinely fit the asynchronous model.

Real-world example: customer-support bot at 1M requests/month

Assume a customer-support bot handling 1 million requests per month, each with an average of 2,000 input tokens and 500 output tokens. This is a realistic profile for a mid-size SaaS company with active users across time zones.

Scenario A — Standard pricing, no optimizations:

GPT-4o: (2,000 × $2.50 + 500 × $10.00) ÷ 1,000,000 × 1,000,000 = $10,000/month
Claude 3.5 Sonnet: (2,000 × $3.00 + 500 × $15.00) ÷ 1,000,000 × 1,000,000 = $13,500/month

Scenario B — Batch API enabled (50% off input + output):

GPT-4o batch: $5,000/month (saving $5,000 vs. Scenario A)
Claude batch: $6,750/month (saving $6,750 vs. Scenario A)

Scenario C — Batch API + prompt caching (1,000-token system prompt cached, 1,000 tokens regular input):

GPT-4o: 1,000 regular input + 1,000 cache-read input; cache read = $1.25/1M. Total input cost: (1,000 × $1.25 + 1,000 × $1.25) ÷ 1M × 1M = $2,500 input + $2,500 output (batch) = $5,000/month. (GPT-4o cache read and batch input happen to be the same rate.)
Claude: 1,000 regular batch input + 1,000 cache-read input. Cost: (1,000 × $1.50 + 1,000 × $0.30) ÷ 1M × 1M = $1,800 input + $3,750 output (batch) = $5,550/month. Claude closes most of the gap versus GPT-4o once caching is in play.

With a larger cached system prompt (say, 5,000 tokens), Claude's $0.30 cache-read rate would produce even more dramatic savings relative to GPT-4o's $1.25 cache-read rate. The break-even point depends on your prompt size and hit frequency — use the calculator above to model your specific numbers.

Migration tips

Switching between GPT-4o and Claude 3.5 Sonnet is not a drop-in swap. The two providers have different API shapes, prompt conventions, and tokenizer behaviors. Here is what to check before migrating production traffic.

Request/response shape. OpenAI uses a messages array with role (system, user, assistant) and content fields. Anthropic uses a similar messages array but moves the system prompt to a top-level system parameter. Tool definitions also differ: OpenAI uses tools with JSON Schema; Anthropic uses tools with a slightly different schema format. Most AI SDK wrappers (LangChain, LlamaIndex, Vercel AI SDK) abstract this, but raw API callers need to update request construction.
Prompt formatting. Claude responds significantly better to prompts that use XML-style delimiters such as <instructions>, <context>, and <example> tags to separate sections. GPT-4o is tuned on markdown-heavy formatting and handles headers, bullet points, and code fences well but does not benefit as much from XML tags. Expect to rewrite or at least re-test your system prompts when switching directions.
Tokenizer differences. For typical English prose, Claude tokens are approximately 0.8× the GPT-4o token count for the same text — meaning Claude uses fewer tokens to represent the same content. This ratio varies for source code (where Claude can be more token-efficient on verbose languages like Java), and for non-English text (where the gap closes or reverses for some scripts). Budget for these differences when setting max-token limits or estimating costs on a per-word basis.
Context window. Claude 3.5 Sonnet supports 200K tokens versus GPT-4o's 128K. If you are currently truncating long documents for GPT-4o, you may be able to relax those limits on Claude without additional chunking logic.
Test on your evals, not benchmarks. Public benchmarks measure average performance across diverse tasks. Your use case may skew heavily toward one model's strengths. Run at least 200–500 samples from your real production distribution before committing to a migration.

Frequently asked questions

Which is cheaper for batch inference at 1M tokens/day? +

At 1M input tokens per day with batch API enabled, GPT-4o costs $1.25 per million input tokens versus Claude 3.5 Sonnet at $1.50 per million. For output-heavy workloads the gap widens: GPT-4o batch output is $5.00/1M versus Claude batch output at $7.50/1M. However, Claude prompt caching (reads at $0.30/1M) can flip this equation dramatically if your prompts contain a large reusable system prompt.

Does GPT-4o have prompt caching like Claude? +

Both models support prompt caching, but the mechanics differ. OpenAI applies caching automatically on eligible inputs (repeated prefixes) and charges $1.25/1M for cache reads with no separate write fee. Anthropic requires you to explicitly mark cache breakpoints using cache_control in your API request; writes cost $3.75/1M and reads cost $0.30/1M. Claude cache reads are significantly cheaper, making Anthropic caching far more economical when a large system prompt is reused many times per day.

What about Claude 3.5 Haiku and GPT-4o Mini for cheaper tiers? +

If your use case tolerates a smaller model, GPT-4o Mini ($0.15/$0.60 per 1M in/out) and Claude 3.5 Haiku ($0.80/$4.00 per 1M in/out) are both dramatically cheaper than their flagship siblings. GPT-4o Mini wins on raw price for most token ratios. Claude 3.5 Haiku is still competitive for coding and reasoning tasks where quality matters more than cost alone.

Can I trust this calculator for production budgeting? +

This calculator uses pricing verified against the official OpenAI and Anthropic pricing pages as of May 2026. It is suitable for planning and estimating monthly spend. For production budgets, always cross-check against your provider dashboard, account for any committed-use discounts or enterprise pricing you have negotiated, and include a 10–20% buffer for unexpected usage spikes.

What about latency — is Claude faster? +

Latency depends on region, time of day, and request size. In independent benchmarks (Artificial Analysis, May 2026), GPT-4o typically achieves lower time-to-first-token on the OpenAI direct API, especially via Azure OpenAI which has more global edge regions. Claude 3.5 Sonnet on Anthropic API has comparable median latency but shows more variance on long output requests. If latency is critical, run your own benchmarks with your actual prompt/response lengths.

Are there differences in how I structure prompts for each model? +

Yes. Claude performs best with explicit XML-style tags to delineate sections (e.g., <instructions>, <context>, <examples>). GPT-4o uses the standard OpenAI messages array with system/user/assistant roles and supports JSON mode and structured outputs natively. Claude also counts tokens slightly differently from GPT-4o — for typical English prose, Claude tokens are roughly 0.8× the GPT token count, but this ratio varies for code and non-English text.