TokenPad

Claude Sonnet 5 vs Gemini 3.5 Flash

Anthropic against Google, on the three things that decide the bill: what each token costs, how many of them fit, and whether you can count them accurately before you send.

A quality-first workhorse against a context-first one, which is the trade most retrieval systems are making.

Claude Sonnet 5 and Gemini 3.5 Flash compared
 Claude Sonnet 5Gemini 3.5 Flash
ProviderAnthropicGoogle
Input, per 1M tokens$2.00$1.50
Cached input, per 1M$0.2000
Output, per 1M tokens$10.00$9.00
Context window1M
Token countingEstimateEstimate
1K in + 500 out$0.007000$0.006000
Price verified2026-08-032026-08-03

Which one costs less

On a representative request of 1,000 input and 500 output tokens, Gemini 3.5 Flash comes to $0.006000 against $0.007000 — a factor of 1.2.

Context and token counting

Context windows are comparable here, so the decision comes down to price and output quality rather than capacity. Check your real context size in the context window calculator.

The same two models at a million requests a month

One sample request settles nothing, because the two providers price input and output on different ratios. These are four representative workload shapes, each at a million requests a month, computed from the rates in the table.

Monthly cost at one million requests, by workload shape
WorkloadClaude Sonnet 5Gemini 3.5 FlashDifference
Classification500 in · 50 out$1,500.00$1,200.00$300.00 (1.3×)
Chat turn1500 in · 300 out$6,000.00$4,950.00$1,050.00 (1.2×)
Document summary20.000 in · 800 out$48,000.00$37,200.00$10,800.00 (1.3×)
Code generation2000 in · 1500 out$19,000.00$16,500.00$2,500.00 (1.2×)

Gemini 3.5 Flash is cheaper on all four shapes, by between 1.2× and 1.3×. The gap is widest on document summary and narrowest on code generation, so the saving is largest exactly where the volume usually is.

The absolute figures matter as much as the ratios. On document summary alone the difference is $10,800.00 a month — the kind of number that justifies an afternoon of measurement before the decision rather than after it.

What this comparison deliberately does not say

It does not say which one is better. Published benchmark scores are run on public sets that models may have seen, they are reported by parties with an interest in the result, and none of them are measured on your data. Repeating them here would add confident-sounding numbers with nothing behind them.

Price, context window and tokenizer availability are facts, with sources and dates attached. Quality is a measurement you have to take yourself — build an evaluation set with the eval dataset builder and run both models against your own cases. Thirty real examples will tell you more than any leaderboard.

If a migration between these two is what you are weighing, the model migration checklist covers what changes beyond price: tool calling formats, stop reasons, refusal behaviour and the re-tuning that prompts usually need.

Price your own workload

The table above uses representative shapes, not yours. Put your real request volume, prompt size and output length into the cost calculator, and measure a real prompt in the token counter first so the inputs are counted rather than guessed. Prices above were read from each provider’s documentation on August 3, 2026 and August 3, 2026; follow the source links to confirm before committing spend.

Questions

Is Gemini 3.5 Flash always cheaper than Claude Sonnet 5?

On every workload shape priced above, yes — Gemini 3.5 Flash is cheaper, by between 1.2× and 1.3× depending on how output-heavy the work is. Price is not the only criterion, but on this pair it does not reverse.

Can I count tokens for Claude Sonnet 5 and Gemini 3.5 Flash before sending?

Neither exactly. Neither provider publishes a browser-capable tokenizer, so any count made before sending is an estimate derived from a related encoding. The methodology page sets out the basis and the confidence.

How current are these prices?

Claude Sonnet 5 was read from Anthropic's own pricing page on August 3, 2026 and Gemini 3.5 Flash from Google's on August 3, 2026. Both source links are in the table above. Model prices fall without notice, so confirm at the source before committing to a volume decision.

Does the cheaper model produce worse answers?

Not necessarily, and this page does not claim to know. Published benchmark scores are not reproducible on your data, so the only honest measurement is your own evaluation set on your own cases. Price is the part that can be stated as fact; quality is the part you have to measure.

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