Claude vs Gemini

Pricing

Claude 3.5 Sonnet: $3 in / $15 out per 1M. Gemini 1.5 Pro: $1.25 in / $5 out (≤128K). Gemini is ~2.4× cheaper at equivalent context lengths.

Context window

Sonnet: 200K. Gemini: 1M standard, 2M on request. For genuinely massive documents, Gemini is unmatched.

Quality

Claude leads on nuanced writing and code review. Gemini leads on factual recall over long context. For RAG-style retrieval-only workloads, the difference is marginal.

Verdict

Choose Claude for craft. Choose Gemini for scale and price.

A stated workload, priced both ways

At 12M input tokens and 4M output tokens a month: Claude 3.5 Sonnet = (12M x $3) + (4M x $15) = $36 + $60 = $96. Gemini 1.5 Pro = (12M x $1.25) + (4M x $5) = $15 + $20 = $35. Sonnet costs $61/month more, roughly 2.7x, at this 3:1 input:output ratio.

Output tokens drive most of the difference

Output is 3x more expensive on Sonnet ($15 vs $5 per 1M) versus only 2.4x more expensive on input ($3 vs $1.25). In the example above, output accounts for $60 of Sonnet's $96 total (62.5%) but only $20 of Gemini's $35 total (57%) — meaning generation-heavy tasks (long drafts, code, transcripts) widen the price gap faster than retrieval-style, input-heavy tasks do.

There's no price break-even here

Because Gemini 1.5 Pro is cheaper on both input and output at these catalog rates, there is no token ratio where Sonnet becomes the lower-cost option on price alone. Any real-world break-even would come from Sonnet needing fewer retries or shorter follow-up calls to reach an acceptable output — something this pricing table can't quantify. Test that on your own eval set.

When the cheaper model is a false economy

If Gemini's lower per-token cost is paired with outputs that need more editing, more follow-up calls, or a second model pass, the effective cost per finished output can climb back toward or past Sonnet's $96. A 2x increase in call volume to reach usable output, for instance, would put Gemini at $70 — still cheaper here, but the margin shrinks fast, so this is worth verifying rather than assuming.

Running this on your own numbers

Enter your actual monthly input and output token counts into TokenAtlas for both models, then re-run the comparison separately for any workload with a notably different input:output ratio (e.g., long-document summarization vs. short-answer chat) — as shown above, the ratio changes how much of the total gap comes from output pricing.

Frequently asked questions

Which has better function calling?

Claude 3.5 Sonnet is slightly ahead on multi-tool orchestration; Gemini is improving rapidly.

Does Gemini hallucinate less on long context?

Both score well above 95% needle-in-haystack at 200K. Above that, Gemini holds up better.

Can I route between them?

Yes — Pro plans support per-task routing.