Kimi K3 vs GPT-5.6 vs Claude: Full Comparison (2026)

Picking a frontier AI model in mid-2026 comes down to four names: Kimi K3, GPT-5.6 Sol, Claude Fable 5, and Claude Opus 4.8. We pulled every published benchmark number from Moonshot’s launch suite (which tested all models at max thinking effort) plus independent rankings from Artificial Analysis — and compared them head to head, including the cost per completed task that most comparisons ignore.

Update (Sept 2026): This comparison covers the July launch lineup. For the newer models, see Kimi K3 vs GPT-6 Astra, Kimi K3 vs Claude Fable 5.1 and Kimi K3 vs Claude Opus 5.5. On Artificial Analysis’s current Intelligence Index (v4.3.2), K3 scores 44 against Opus 5.5 (54), GPT-6 Astra (53) and Fable 5.1 (51).

Short answer: GPT-5.6 Sol and Claude Fable 5 still lead on overall aggregate intelligence. But Kimi K3 wins 5 of 6 real-world agentic benchmarks outright, costs ~50–65% less per completed task, and is the only one with open weights. For most developers and agentic workloads, K3 is the value king of 2026.

Quick comparison table

	Kimi K3	GPT-5.6 Sol	Claude Fable 5	Claude Opus 4.8
Benchmark wins (of 6)	5	1	0	0
Terminal-Bench 2.1	88.3	88.8	84.6	84.6
Program Bench	77.8	77.6	76.8	71.9
SWE Marathon	42.0	39.0	35.0	40.0
BrowseComp	91.2	90.4	88.0	84.3
SpreadsheetBench 2	34.8	32.4	34.7	31.6
Automation Bench	30.8	29.7	29.1	27.2
Cost per task	$0.94	~$1.90+	$2.75	~$1.90
Context window	1M tokens	—	1M tokens	200K-class
Open weights	Yes (released July 27)	No	No	No
Vision / video	Native	Native	Native	Native

Cost-per-task figures from Moonshot’s launch data; K3 is stated as ~50% cheaper per task than Opus 4.8 / GPT-5.5 and ~3× cheaper than Fable 5.

Benchmark-by-benchmark breakdown

All scores below are from Moonshot’s official launch charts (July 2026), all models at maximum thinking effort. Moonshot’s own footnotes flag that Fable 5 results include potential fallback behavior and GPT-5.6 Sol results include potential cyberguards.

Coding agents: Terminal-Bench 2.1 & Program Bench

Terminal-Bench 2.1 is the one benchmark K3 doesn’t win — but it loses by just 0.5 points (88.3 vs GPT-5.6 Sol’s 88.8), while both Claude models tie at 84.6 and GPT-5.5 trails at 83.4.

Program Bench flips the order: K3 takes #1 at 77.8, edging GPT-5.6 Sol (77.6) and Fable 5 (76.8), with Opus 4.8 well behind at 71.9.

Verdict for coding: effectively a tie between K3 and GPT-5.6 Sol at the top — but K3 delivers it at roughly half the cost per task. K3 also ranks #1 on the Frontend Code Arena leaderboard.

Long-horizon software engineering: SWE Marathon

This is K3’s most decisive win. K3 scores 42.0, beating Claude Opus 4.8 (40.0), GPT-5.6 Sol (39.0), and Fable 5 (35.0) — and nearly tripling GPT-5.5 (14.0). SWE Marathon tests sustained, multi-step engineering work, and it’s where K3’s KDA architecture and 1M-token context pay off most.

Web research agents: BrowseComp

K3 leads again: 91.2 vs GPT-5.6 Sol’s 90.4, Fable 5’s 88.0, GPT-5.5’s 84.4 and Opus 4.8’s 84.3. Moonshot notes BrowseComp was run with context compaction at 300K; with the full 1M-token window K3 scores 90.4–91.2 — suggesting even more headroom.

Office work: SpreadsheetBench 2 & Automation Bench

Two more photo-finishes at the top: K3 wins SpreadsheetBench 2 by 0.1 points (34.8 vs Fable 5’s 34.7) and Automation Bench by 1.1 (30.8 vs GPT-5.6 Sol’s 29.7). These are the benchmarks closest to real business workflows — spreadsheets, dashboards, repetitive automation — and K3 wins both.

The honest part: where K3 doesn’t win

Aggregate intelligence rankings tell a different story than individual benchmarks. At launch, on Artificial Analysis’s independent index, K3 ranked #4 of 189 models (score 57) — behind GPT-5.6 Sol and Claude Fable 5 (though ahead of Claude Opus 4.8 and GPT-5.5). Moonshot itself said the same thing in its launch materials. AA has since re-versioned the index: on v4.3.2, K3 scores 44 (#3 of 115 large open-weight models), and the two scores are not comparable.

So be skeptical of anyone claiming “K3 beats GPT-5.6” full stop. The accurate claim is:

• Agentic / real-world task benchmarks: K3 wins 5 of 6
• Overall aggregate intelligence: GPT-5.6 Sol and Fable 5 still lead
• Cost-adjusted performance: K3 wins comfortably

Cost per completed task

Benchmarks don’t pay invoices — cost per completed task does:

Model	Approx. cost per task
Kimi K3	$0.94
Claude Opus 4.8 / GPT-5.5	~2× K3
Claude Fable 5	$2.75 (~3× K3)

K3’s advantage comes from Mooncake disaggregated inference with >90% cache-hit rates in coding workloads (cache-hit input is just $0.30/1M tokens vs $3.00 cache-miss; output $15.00/1M). If you’re running agents at scale, a 50–65% cost gap compounds fast.

Context window: K3’s structural advantage

K3 ships a 1,048,576-token context window with decoding up to 6.3× faster than its predecessor at long contexts — matching the largest windows in this lineup (Fable 5 also offers 1M) and well beyond Opus 4.8’s 200K class. If your workload is whole-codebase analysis, book-length documents, or long video transcripts, this alone can decide the comparison. Details in our K3 specifications breakdown.

Openness: a category of one

K3’s weights were released July 27, 2026 on Hugging Face under a custom Kimi K3 License, which allows commercial use and fine-tuning (Model-as-a-Service businesses above $20M in 12-month revenue need a separate agreement with Moonshot) — the largest open-weight model ever (2.8T parameters). Details in our open weights guide. GPT-5.6 Sol, Fable 5, and Opus 4.8 are all closed, API-only products. For companies with compliance, data-residency, or fine-tuning needs, K3 is the only option in this comparison.

Which model should you choose?

Choose Kimi K3 if:

• You run agentic/coding workloads at scale (best benchmark-per-dollar, 5 of 6 wins)
• You need a 1M-token context window
• You need open weights for self-hosting or fine-tuning
• Budget matters — ~50–65% cheaper per task

Choose GPT-5.6 Sol if:

• You want the top overall aggregate intelligence score and cost is secondary
• You’re already deep in the OpenAI ecosystem

Choose Claude Fable 5 if:

• You need the strongest overall reasoning regardless of price ($2.75/task)
• You’re invested in Anthropic’s tooling

Choose Claude Opus 4.8 if:

• You want a proven, stable model — but note it places behind K3 on 5 of 6 benchmarks at roughly double the per-task cost

For most new projects in 2026, K3 is the rational default — and validating it is cheap: Kimi’s free tier is enough for a first look, confirmed K3 access needs a paid plan, and the API unlocks after a $1 top-up, so you can test it on your own workload before committing. See how to get it on PC or start from our complete Kimi K3 guide.

FAQ

Is Kimi K3 better than GPT-5.6? On real-world agentic benchmarks, mostly yes — K3 wins 5 of 6 head-to-head tests against standard Sol. On overall aggregate intelligence rankings (Artificial Analysis), GPT-5.6 Sol still leads. K3 costs roughly half as much per completed task. Full deep-dive: Kimi K3 vs GPT-5.6 Sol, Terra & Luna.

Is Kimi K3 better than Claude Opus 4.8? Yes on the numbers: K3 beats Opus 4.8 on 5 of 6 benchmarks (tying effectively on Terminal-Bench) at about half the cost per task.

Kimi K3 vs Claude Fable 5 — which wins? K3 wins all 6 head-to-head benchmarks against Fable 5 and costs ~3× less per task ($0.94 vs $2.75). Fable 5 retains an edge on aggregate intelligence rankings. Read the dedicated deep-dive: Kimi K3 vs Claude Fable 5.

What about GPT-5.5? It trails K3 on every benchmark in this comparison — most dramatically SWE Marathon (14.0 vs 42.0) — at roughly double the per-task cost. K3 is the clear upgrade.

Are these benchmarks independent? The 6 benchmark charts are from Moonshot’s launch suite (all models at max thinking effort, with footnoted caveats). At launch, the independent Artificial Analysis index placed K3 #4 of 189 models (score 57) on its previous version; on the current v4.3.2 index it scores 44, #3 of 115 large open-weight models.