Kimi K3 vs GPT-5.6 (Sol, Terra, Luna): Verdict (2026)

One week. That’s all that separated the two biggest AI launches of July 2026: GPT-5.6 went generally available on July 9, and Kimi K3 launched on July 16. OpenAI’s new flagship, GPT-5.6 Sol, is the model Moonshot measured K3 against in every launch benchmark — and the results make this the closest open-vs-closed race the industry has seen.

But comparing “K3 vs GPT-5.6” isn’t simple, because GPT-5.6 isn’t one model — it’s three (Sol, Terra, Luna), and OpenAI’s flashiest benchmark numbers come from a special Ultra mode that most coverage doesn’t explain. This article untangles all of it: every head-to-head score, the Ultra asterisk, exact pricing across all tiers, independent arena data, and a clear verdict.

First, understand the GPT-5.6 family

OpenAI changed its naming strategy with this release: 5.6 is the generation; Sol, Terra and Luna are capability tiers that will advance on their own schedules. All three share a 1M+ token context window and 128K max output, distilled from the same base training run but optimized for different price-performance points:

	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Role	Flagship	Balanced everyday	Fast & cheap
Roughly equivalent to	New frontier	GPT-5.5 (at half cost)	High-volume tasks
Input (per 1M)	$5.00	$2.50	$1.00
Output (per 1M)	$30.00	$15.00	$6.00
Reasoning levels	none → xhigh, max, plus Ultra multi-agent	Same levels	Same levels
Best for	Complex professional & agentic work	Production workloads	Latency/cost-sensitive volume

The gpt-5.6 API alias routes to Sol. Sol is available fully to ChatGPT Pro subscribers and API users, rate-limited on Plus, and absent from the free tier. Since Kimi K3 is a frontier-class model, Sol is the fair head-to-head — but Terra and Luna matter to this comparison’s pricing math, as you’ll see below.

Kimi K3 vs GPT-5.6 Sol at a glance

	Kimi K3	GPT-5.6 Sol
Maker	Moonshot AI	OpenAI
Released	July 16, 2026	July 9, 2026 (GA)
Architecture	2.8T MoE, 16/896 experts (~50B active)	Undisclosed (closed)
Context window	1M tokens	1.05M tokens
Max output	—	128K tokens
Thinking	Max effort (default; more modes coming)	none/low/med/high/xhigh/max + Ultra
Input price	$3.00 ($0.30 cached)	$5.00 ($0.50 cached)
Output price	$15.00	$30.00
Cost per benchmark task	$0.94	~$1.90
Open weights	Yes — July 27, Modified MIT	No
Free tier	Yes — full K3 in Kimi app	No (Sol is paid-only)

The head-to-head benchmarks: K3 wins 5 of 6

Moonshot’s launch suite tested every model at maximum thinking effort, each under its own designated harness (K3 under KimiCode, Sol under Codex):

Benchmark	Kimi K3	GPT-5.6 Sol	Winner
Terminal-Bench 2.1	88.3	88.8	Sol by 0.5
Program Bench	77.8	77.6	K3 by 0.2
SWE Marathon	42.0	39.0	K3 by 3.0
BrowseComp	91.2	90.4	K3 by 0.8
SpreadsheetBench 2	34.8	32.4	K3 by 2.4
Automation Bench	30.8	29.7	K3 by 1.1

The pattern: Sol’s only win is a 0.5-point edge on Terminal-Bench, while K3’s wins include decisive gaps on SWE Marathon (long-horizon engineering) and SpreadsheetBench 2 (real office work). Two footnotes matter here:

• Sol’s cyberguard activated on 10% of tasks in Moonshot’s KCB 2.0 coding benchmark — OpenAI’s cybersecurity safeguards refused or constrained those runs, dragging its score. If your workloads touch security-sensitive code, this affects production too, not just benchmarks.
• K3’s BrowseComp 91.2 was scored with context compaction at 300K — less than a third of its window. With the full 1M window it scores 90.4–91.2 without any compaction strategy.

The Ultra asterisk you need to understand

OpenAI’s marketing headlines quote higher numbers: 91.9% on Terminal-Bench 2.1 and 92.2% on BrowseComp — both above K3. What’s often missed: those are Sol Ultra results.

Ultra is a multi-agent mode that spins up multiple sub-agents working in parallel, then merges their findings — OpenAI’s own analogy is assigning research, writing, editing and fact-checking to four people instead of one. It’s genuinely powerful, but it’s a fundamentally different (and far more compute-hungry) operating mode. Moonshot’s suite tested standard Sol — the mode whose price is $5/$30 — and against standard Sol, K3 wins 5 of 6.

The fair takeaway: if you let Sol burn significantly more compute per answer via Ultra, it can edge past K3’s single-agent max mode. On equal footing, K3 leads. And K3’s low/high effort modes haven’t even shipped yet.

What OpenAI’s own numbers say (and don’t)

OpenAI reported strong results for Sol on suites where K3 has no published scores yet:

• SWE-bench Pro: 64.6% (vs Fable 5’s 80.3% — interestingly, Anthropic leads here)
• GPQA Diamond: 94.6% graduate-level science reasoning
• OSWorld 2.0: 62.6% computer-use
• DeepSWE: 72.7 (xhigh, with tools)
• Artificial Analysis Coding Agent Index: Sol 80 vs Fable 5’s 77.2 — OpenAI claims SOTA, reached with fewer than half the output tokens, half the time, and a third of the cost of Fable 5

Sam Altman also claims Sol delivers 54% better token efficiency on AI coding tasks versus the previous generation. Until independent testers publish K3 on these same suites, treat this category as “unproven either way” — what’s confirmed is that on the independent Artificial Analysis aggregate index, K3 (#4 of 189) still sits behind Sol, and Moonshot itself says K3 trails Sol and Fable 5 in overall user experience.

The people’s vote: LMArena

Blind community voting tells a cleaner story than vendor charts:

	Kimi K3	GPT-5.6 Sol
Frontend Code Arena rank	#1	#3
Elo	1679	1590
Pairwise win rate	76%	58%

K3 jumped 17 places from K2.6 to take #1 — above every closed model, Sol included. On the general Text Arena, though, K3 ranks #9; the closed flagships still own general chat.

Pricing: K3 vs the whole GPT-5.6 ladder

Per 1M tokens	Kimi K3	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Input	$3.00	$5.00	$2.50	$1.00
Cached input	$0.30	$0.50	—	—
Output	$15.00	$30.00	$15.00	$6.00
Batch discount	—	50%	—	—

Versus Sol, K3 is 1.67× cheaper on list prices — but the real-world gap is bigger: measured cost per benchmark task is $0.94 for K3 vs roughly $1.90 for Sol, because Moonshot’s Mooncake inference achieves >90% cache-hit rates in coding workloads, billing most input at the $0.30 tier.

The more interesting fight is K3 vs Terra: Terra matches GPT-5.5-level performance at $2.50/$15 — K3’s exact output price. But K3 demolished GPT-5.5 in Moonshot’s suite (SWE Marathon 42.0 vs 14.0; wins on all six). At identical output pricing, K3 delivers frontier-class agentic performance where Terra delivers last-generation performance. Luna ($1/$6) undercuts everyone on price but targets high-volume simple tasks — a workload K3 at max thinking isn’t designed for anyway.

One genuine Sol advantage: a 50% Batch API discount for offline workloads, which Moonshot doesn’t offer yet.

Modes, safety and the cybersecurity angle

Sol ships with the most granular control in the industry: six reasoning levels (none → max) plus Ultra multi-agent. K3 launched locked at max thinking effort, with low/high modes promised later — today, Sol gives you more dials.

Both launches were shaped by cybersecurity politics. Sol’s release was delayed weeks by a US government review (the Commerce Department’s AI Standards center tested it before launch), and OpenAI markets Sol as its “most powerful cybersecurity model” — matching Anthropic’s restricted Mythos preview on ExploitBench with ~3× fewer tokens, positioned for defensive work like threat modeling and blue teaming. Those same safeguards produced the cyberguard refusals seen in benchmarks. K3 takes the opposite philosophy: open weights for everyone on July 27, with the responsibility shifted to deployers.

Ecosystem: convenience vs freedom

Choose Sol’s world if you want: ChatGPT’s polished UX, Codex, the Responses API with web/file search and computer-use tools, Azure/Bedrock availability, ChatGPT Work for office tasks, and OpenAI’s enterprise compliance machine.

Choose K3’s world if you want: a free tier with the full flagship model (no credit card), Kimi Work’s widgets and dashboards, Kimi Code’s CLI agent, an OpenAI-compatible API at a third of the task cost — and after July 27, the weights themselves (Modified MIT, MXFP4) for self-hosting and fine-tuning. Sol offers no fine-tuning and will never be open.

Which should you choose?

Your situation	Pick
Agentic coding at production scale (5/6 benchmark wins, ~50% cheaper per task)	Kimi K3
Frontend/UI development (#1 Arena, 1679 vs 1590 Elo)	Kimi K3
Terminal-heavy CLI automation	GPT-5.6 Sol (narrowly) or K3 — near tie
Maximum reasoning regardless of compute cost	Sol Ultra (multi-agent)
Granular reasoning-effort control today	GPT-5.6 Sol
GPT-5.5-class workloads at K3’s price	Kimi K3 over Terra
High-volume simple tasks	GPT-5.6 Luna
Self-hosting, fine-tuning, zero lock-in	Kimi K3 (July 27)
ChatGPT/Codex ecosystem investment	GPT-5.6 Sol

The zero-cost move: K3 is free with full capability in the Kimi app — an invite link adds bonus membership credits. Sol has no free tier, so you can benchmark K3 against your real workload before spending a dollar on either API.

FAQ

Is Kimi K3 better than GPT-5.6 Sol? On Moonshot’s agentic suite, K3 beats standard Sol on 5 of 6 benchmarks (losing Terminal-Bench by 0.5). Sol’s higher headline numbers (91.9 Terminal-Bench, 92.2 BrowseComp) come from Ultra multi-agent mode, which uses far more compute. On aggregate intelligence rankings, Sol still leads; K3 costs about half as much per task.

What’s the difference between GPT-5.6 Sol, Terra and Luna? They’re capability tiers of the same generation: Sol is the flagship ($5/$30 per 1M), Terra matches GPT-5.5 at half its cost ($2.50/$15), and Luna is the fast budget tier ($1/$6). All share a 1M+ context window. K3 competes with Sol on quality — and beats Terra’s performance class at Terra’s output price.

Is Kimi K3 cheaper than GPT-5.6? Yes: $3/$15 vs Sol’s $5/$30 per 1M tokens, and roughly half the measured cost per task ($0.94 vs ~$1.90) thanks to >90% cache-hit rates. Luna is cheaper per token than K3 but targets simpler high-volume work, not frontier agentic tasks.

Kimi K3 vs GPT-5.6 Sol for coding? K3 for most teams: it wins SWE Marathon (42.0 vs 39.0), Program Bench, and the community Frontend Code Arena (76% vs 58% win rate) at half the task cost. Sol keeps a 0.5-point Terminal-Bench edge and the Ultra mode for maximum-effort runs.

Can Kimi K3 replace ChatGPT/Codex? For API-driven agents and coding pipelines — very plausibly, at half cost, with open weights for self-hosting after July 27. If your team lives in ChatGPT’s UX, Codex, or Azure, Sol is the smoother fit. K3’s Kimi Code CLI is the closest equivalent to Codex.

Which is safer for enterprise use? Different models of safety: Sol passed a US government pre-launch review and ships restrictive cyberguards (which can refuse security-adjacent tasks). K3 is open-weight — you control deployment, data never leaves your infrastructure if self-hosted, but you own the responsibility.

The verdict

Kimi K3 vs GPT-5.6 Sol is a genuine split decision — and that alone is historic for an open-weight model. Against standard Sol, K3 wins five of six agentic benchmarks, leads the community coding arena, and costs roughly half per task. Sol answers with Ultra mode’s multi-agent ceiling, finer control knobs, a 50% batch discount, and the deepest product ecosystem in AI. Terra and Luna fill price points K3 doesn’t target — but at K3’s own price, nothing in OpenAI’s lineup matches K3’s performance.

Also see: Kimi K3 vs Claude Fable 5 (the other flagship fight), the four-way frontier comparison, and the complete Kimi K3 guide.