Google vs Moonshot · synced 2026-08-11

gemini-3.5-flash vs kimi-k2.6

kimi-k2.6 is 1.6× cheaper on input than gemini-3.5-flash.

gemini-3.5-flash kimi-k2.6
Input / 1M $1.50 $0.95
Cached input / 1M $0.15 $0.16
Output / 1M $9.00 $4.00
Context window 1M 262K
Max output 66K 262K
Provider Google Moonshot
Vision Yes Yes
Tool calling Yes Yes
Token count estimated estimated

Capability for the money

Independent measurement · Artificial Analysis · read 2026-08-11

gemini-3.5-flash costs 2.0× more than kimi-k2.6 without measurably beating it on any benchmark they share — on that evidence the extra spend buys nothing you can point at.

  1. Following instructions

    extraction, classification, sticking to a format

    IFBench
    gemini-3.5-flash76.3%
    kimi-k2.676.0%

    gemini-3.5-flash +0.3

  2. Tool use

    multi-step agent work against real tools

    τ²-bench
    gemini-3.5-flash95.3%
    kimi-k2.695.9%

    kimi-k2.6 +0.6

  3. Hard reasoning

    graduate-level science and multi-step analysis

    GPQA Diamond
    gemini-3.5-flash92.2%
    kimi-k2.691.1%

    gemini-3.5-flash +1.1

Averaged over the benchmarks both models sat, kimi-k2.6 returns 51.2 points of pass rate per dollar of blended price against 26.1 — the ratio that decides whether the dearer model earns its rate.

Each figure is a pass rate: the share of that test set the model answered correctly, measured by a third party rather than self-reported by the lab. Rows are different tests of different difficulty, so compare a model against the other model, not one row against the next. No published coding score for one of these models, so that lens is omitted. And a benchmark is not your workload — it narrows the shortlist, it does not pick for you.

Which to pick

List price is not production cost. Output length, cache hit rate, retries and task quality move the real number more than the headline rate does — price your own prompt on the token ledger before deciding.

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