Kimi K3 vs GPT-5.6 (Sol, Terra, Luna): Price, Rank & Code

Two frontier launches landed a week apart in July 2026: Moonshot AI shipped Kimi K3 on 2026-07-16, and OpenAI made GPT-5.6 generally available on 2026-07-09. The tidy narrative is that one of them wins. The honest answer is that they disagree with themselves: on independent general-intelligence scoring GPT-5.6 Sol edges K3, while on a blind human vote for frontend code K3 comes out on top. Below we separate what the vendors claim from what independent evals measured, then add our own launch-day cost-per-task run so you can see who is actually paying off.

The verdict in 30 seconds

Kimi K3 and GPT-5.6 Sol are close enough that the right pick depends on your workload, not on a single headline rank. Here is the honest core tension, with every number labeled by where it came from:

• Independent general intelligence: GPT-5.6 Sol scores 59 on the Artificial Analysis Intelligence Index; Kimi K3 scores 57. Sol ranks above K3. Both sit just under Claude Fable 5 at about 60. (Independent: Artificial Analysis.)
• Independent frontend code, blind human vote: K3 is number one on Arena.ai's Frontend Code Arena at 1,679 points, ahead of Fable 5 at 1,631, with a 76% pairwise win rate. (Independent, preliminary blind Elo-style score.)
• Price: K3 is $3 / $15 per million tokens; Sol is $5 / $30. K3 is roughly 40% cheaper. (Vendor list prices.)
• Open weights: K3's weights are promised for 2026-07-27 and were not downloadable on launch day. Not yet a shipped fact.

So: on independent general intelligence Sol edges K3, but K3 is cheaper, wins the blind frontend code arena, and dangles open weights that had not yet shipped. The two independent evals genuinely disagree on the winner, and that disagreement is the story.

Specs, price & context

Here is the head-to-head. Prices are vendor list prices per million tokens. We show K3 against GPT-5.6 Sol because Sol is the tier that competes with K3 on capability; Terra and Luna are cheaper OpenAI tiers covered below.

A note on GPT-5.6 pricing precision: the Sol / Terra / Luna list prices above come from reputable secondary sources because OpenAI's own pricing page returned an error at the time of writing. The numbers are consistent across those sources, but were not confirmed on openai.com directly — flag that if a fraction of a cent matters to your budget. Full tier breakdown in our GPT-5.6 explainer.

Where independent rankings disagree

Two independent evaluators looked at these models and reached opposite conclusions about which is better. That is not a contradiction to resolve in one model's favor — it is a signal that they measure different things.

Artificial Analysis Intelligence Index is a blended index of reasoning, knowledge and general capability. K3 scores 57. There is a small source nuance worth being honest about: AA's launch article framed K3 as #3, but AA's live model page currently reads #4 / 187. Rather than print one hard rank, the fair statement is that K3 lands top-4, just behind Fable 5 (~60) and GPT-5.6 Sol (59). On this axis, Sol is above K3.

Arena.ai's Frontend Code Arena is a blind, head-to-head human vote: raters see two anonymous outputs and pick the better UI code, so brand bias is stripped out. Here K3 is #1 at 1,679 points, ahead of Fable 5 at 1,631, with a 76% pairwise win rate and a 17-place jump from Kimi-k2.6. Label it as a preliminary blind Elo-style score, but it is independent and it favors K3.

The takeaway competitors tend to bury: if your work is general reasoning, Sol has the edge; if it is frontend code specifically, the blind vote goes to K3. Same two models, two honest answers.

Our launch-day cost-per-task run

Independent rankings tell you who is smart. They do not tell you who is cost-effective on your actual work. So on K3's launch day we ran it through our standing coding harness: 9 generate-code-then-run-hidden-tests tasks, where cost equals real token usage times list price, expressed per 1,000 tasks. This is first-party DataLLM Lab data.

Two honest caveats up front. First, our broader 13-model executed run found that 10 of 13 models scored a perfect 9/9 — correctness on these tasks is table stakes, so the real spread is cost, and it was an 88x spread from cheapest to priciest. Second, and this matters: K3 did not score 9/9. It passed every task that executed — 8 of 9 across two runs — but the 9th task, parse_csv_line, never completed because of repeated upstream 429 rate-limiting on launch day. That is a capacity signal, not a correctness miss. We report it as passed all 8 we could execute; the 9th could not be evaluated.

On cost, K3 came in around $4 per 1,000 tasks (one run measured $4.37) — squarely Claude Opus 4.8 territory ($4.05/1k in our run) — and it was notably token-lean, spending only about 150 reasoning tokens per task. That is the interesting result: K3 is priced like a mid-tier model but its real per-task bill lands near a premium one, because output tokens are what you pay for. Full methodology lives in our LLM coding cost benchmark.

Important scope note: GPT-5.6, GPT-5.6 Sol, Grok 4.5 and Fable 5 were not in our executed 13-model run, so we do not attribute any first-party score or cost to them. For those we use vendor and independent numbers only, and label them as such.

What each vendor claims

Both makers put out numbers that flatter themselves. Keep these visually separate from the independent evals above.

Moonshot's own benchmark table (vendor-reported): on Moonshot's published numbers, K3 beats Opus 4.8 and GPT-5.5, but sits just under Fable 5 and GPT-5.6 Sol. Notice that this is directionally consistent with the independent AA index — K3 strong but a step below Sol — which is a good sign, but it is still the vendor grading its own homework.

OpenAI's efficiency claim (vendor-reported): the widely-quoted line that GPT-5.6 is about 54% more token-efficient on agentic coding is a Sam Altman / OpenAI statement from Sun Valley around 2026-07-09. OpenAI did not disclose the comparison baseline — which competing models, which tasks. So it is a marketing figure, not a measured cross-model fact. Our first-party finding that K3 is unusually token-lean (~150 reasoning tokens/task) is the kind of number that would let you test such claims directly, one model at a time.

The rule of thumb: a vendor benchmark tells you what a model can do on a good day on a chosen test; an independent blind eval tells you what raters prefer; a cost-per-task run tells you what you will pay. You want all three, clearly labeled.

Cost per completed task

This is the number that decides most production choices, and it is where the 88x spread lives. The chart shows first-party cost per 1,000 tasks for the models we executed, plus K3's launch-day figure. Correctness was 9/9 for every bar except Grok 4.3 (8/9) and K3 (8 of 8 executed, 1 unevaluated), so read it primarily as a cost ranking.

Which one to pick

A simple decision rule, given what is actually verified as of July 2026:

• Pick GPT-5.6 Sol if you want the highest independent general-intelligence score of the pair (59 vs 57), the largest context window (1.05M), and you are not price-sensitive at $5/$30.
• Pick Kimi K3 if your work is frontend / UI code (it wins the blind arena), if you want roughly 40% lower token prices, or if you are waiting on the promised 2026-07-27 open weights for self-hosting. Just budget for real per-task cost near Opus 4.8, not near its cheap sticker price.
• Step down to GPT-5.6 Terra or Luna, or an open-weight coder like GLM 5.2 or Qwen3 Coder Next, if correctness is table stakes and you mainly care about cost — our run shows most models clear the bar, so paying top-tier prices is optional for a lot of workloads.

Dig deeper in our Kimi K3 review, the Kimi K3 API guide, the GPT-5.6 vs Opus 4.8 comparison, or our roundup of the best open-source LLMs of 2026.

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