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Claude vs GPT vs Gemini: Coding Costs per 1,000 Tasks

Compare the workload, not just the model name

An AI coding assistant can spend more tokens reading your repository than writing code. To compare Claude, GPT and Gemini fairly, estimate input tokens, output tokens and requests per completed task—not simply the price of one prompt.

The examples below use published standard text API prices for specific model versions, not a live quote or a claim about each provider’s newest model. Prices are in US dollars per million tokens. Verify current rates before budgeting; subscriptions, taxes, tools and separate execution charges are excluded.

A concrete coding workload: 20,000 input tokens

Assume each request reads 20,000 tokens of instructions, source files and conversation history, then generates 2,000 output tokens. At 1,000 requests, that means 20 million input tokens and 2 million output tokens.

• Claude Sonnet 4: $3 input and $15 output. Each request costs $0.090; 1,000 requests cost $90.
• GPT-4.1: $2 input and $8 output. Each request costs $0.056; 1,000 requests cost $56.
• Gemini 2.5 Pro: $1.25 input and $10 output at the standard tier for prompts up to 200,000 tokens. Each request costs $0.045; 1,000 requests cost $45.
• GPT-4.1 mini: $0.40 input and $1.60 output. Each request costs $0.0112; 1,000 requests cost $11.20.
• Claude 3.5 Haiku: $0.80 input and $4 output. Each request costs $0.024; 1,000 requests cost $24.

The formula is straightforward: input tokens divided by one million, multiplied by the input rate, plus output tokens divided by one million, multiplied by the output rate. Gemini’s longer-prompt pricing differs, so do not extend this example beyond its stated tier.

Optimize for accepted code, not cheap requests

A lower token price does not guarantee a cheaper finished feature. Route routine transformations, test scaffolding and documentation to a smaller model. Reserve stronger models for architectural decisions, difficult debugging and changes spanning several modules.

Benchmark 30 representative tasks against your own tests. Record total spend, retries and accepted patches. Three GPT-4.1 mini requests at the workload above cost $0.0336, still below one GPT-4.1 request at $0.056. However, extra review time or failed tests can erase that saving. Measure cost per accepted patch alongside latency and developer effort.

Reduce context and exploit caching

• Send relevant files: use search results, function definitions and focused diffs instead of repeatedly attaching the entire repository.
• Keep reusable prefixes stable: place fixed instructions and shared context before changing task content to improve cache eligibility.
• Limit unnecessary output: request a patch and a brief test summary rather than a lengthy explanation.
• Cap autonomous loops: stop repeated failures and escalate instead of paying for endless retries.

For example, GPT-4.1’s published cached-input rate is $0.50 per million tokens. If 15,000 of the 20,000 input tokens qualify as cached, a request costs $0.0075 for cached input, $0.010 for fresh input and $0.016 for output: $0.0335 total. Across 1,000 requests, that is $33.50 instead of $56. Cache hits are not guaranteed; other providers may charge separately for cache creation or storage.

Where 59API can lower Claude and GPT spending

59API offers pay-as-you-go access to Claude and GPT and positions itself among the cheapest AI API relays. It states that it uses native, official-quality models without downgrades, with compatibility for Claude Code, Codex and OpenAI SDKs. Its API base URL is https://api.59api.com; follow its integration instructions for authentication and client-specific endpoint settings.

Compare its actual model rates and billing rules with your workload. If a relay quote were 30% below the Sonnet example’s rates, the same 1,000 requests would cost $63 rather than $90—a $27 saving. That is an illustration, not a quoted 59API discount. Confirm caching support, model identifiers and any referral rebate conditions; do not assume Gemini availability.

If that comparison looks promising, consider signing up for 59API and testing a small budget first. Check patch quality, latency and billed tokens before moving your main coding workflow.

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