Kimi K3 vs GPT-5.6 Sol, Terra, and Luna: Benchmarks, Pricing, and Which Tier to Pick

Kimi K3 competes against an entire family, not a single model. GPT-5.6 ships as three tiers: Sol (flagship, $5/$30), Terra (balanced, $2.50/$15), and Luna (fastest, $1/$6). K3 sits in a different spot against each one. It trails Sol on overall intelligence, outscores Terra across the Artificial Analysis suite and Luna on every shared benchmark, and costs more than both cheaper tiers. The right choice depends on which tier you are actually comparing against.

The GPT-5.6 family explained

GPT-5.6 is not one model. It is three tiers, each architecturally distinct and priced for a different workload. Understanding the tiers matters because K3's competitive position changes dramatically depending on which one you compare it to.

Sol is the flagship. It targets hard reasoning, coding, cybersecurity, and long-horizon agentic work. It includes an Ultra mode that coordinates four parallel sub-agents, the only model in this comparison with that capability. Sol went generally available on July 9, 2026.

Terra is the balanced mid-tier. OpenAI positions it as delivering GPT-5.5-level performance at roughly half the cost. For a detailed look at how the three tiers stack up against each other, see Emergent's GPT-5.6 Sol vs Terra vs Luna comparison.

Luna is the fastest and cheapest tier, built for high-volume tasks like classification, summarization, and batch processing. At $1/$6 per million tokens, it is the budget option in OpenAI's lineup.

Kimi K3 and GPT-5.6 at a glance

Pricing as of July 2026. Sources: Moonshot K3 pricing, OpenAI API pricing.

Benchmark comparison: K3 vs each GPT-5.6 tier

1. K3 vs Sol: the flagship fight

Sol leads on overall intelligence and most shared benchmarks. K3 wins on a handful of agentic and frontend tasks and costs 40% less per token.

Artificial Analysis figures reflect the July 23, 2026 snapshot cited on Moonshot's model card.

One naming trap worth flagging: AutomationBench and AutomationBench-AA are not the same evaluation. Moonshot's card reports AutomationBench on a 600-task public subset, where K3 posts 30.8 and Sol 29.7. Artificial Analysis runs its own implementation of Zapier's agentic SaaS workflow evaluation, called AutomationBench-AA, where K3 scores 53% and takes the top position among tested models. The two sets of numbers measure different things and neither contradicts the other. This article uses Moonshot's card figures in the table above, and the Artificial Analysis figures wherever the -AA suffix appears.

Sol leads 6 of the 10 rows above. The widest Sol advantage is on DeepSWE (+5.5), a repository engineering benchmark. K3's biggest win is FrontierSWE (+9.9), the largest gap on any row in the comparison. On Terminal-Bench 2.1, the models are functionally tied at 88.3 vs 88.8 across different harnesses.

K3 also holds #1 on LMArena's Frontend Code Arena, where blind developer voting ranked it above Claude Fable 5 in 76% of matchups. Sol does not appear in the Frontend Code Arena rankings.

For a comparison of how Sol stacks up against Anthropic's flagship, see GPT-5.6 vs Claude Opus 4.8.

2. K3 vs Terra: the value battle

K3 dominates Terra on every evaluation in the Artificial Analysis suite. The Intelligence Index gap is 8 points (57 vs 49 at high effort) or 17 points (57 vs 40 at low effort). On individual evaluations from Artificial Analysis, K3 leads on every single row where both models have scores.

All scores independently verified by Artificial Analysis, July 2026. Terra high = high effort setting, Terra low = low effort.

On llm-stats, K3 wins 8 of 9 shared benchmarks against Terra, with Terra leading only on DeepSWE (69.6% vs 67.5%).

But Terra has a pricing advantage K3 cannot match on the input side. At $2.50 per million input tokens, Terra is cheaper than K3's $3.00. On output, both charge $15.00 per million tokens. For input-heavy workloads (long context, large repo prefixes), Terra is cheaper per token despite being significantly weaker.

Terra also inherits the full GPT-5.6 family's reasoning controls (none through max) and runs at 130 tokens per second, more than 3x K3's 39 tok/s. For teams that need "good enough" intelligence at high speed and low cost, Terra is a compelling option even though K3 outscores it everywhere.

One area where K3's advantage is especially stark: factual reliability. K3 posts a 49% non-hallucination rate on AA-Omniscience vs Terra's 13% (high effort) or 12% (low effort). Terra fabricates answers far more frequently than K3 does. For workloads where factual accuracy matters, this gap may outweigh Terra's cost advantage.

3. K3 vs Luna: capability vs speed

K3 wins every shared benchmark against Luna, often by wide margins. On llm-stats, K3 leads all 9 of 9 shared benchmarks. The gaps are substantial: AutomationBench (30.8% vs 14.9%), Terminal-Bench 2.1 (88.3% vs 84.7%), Toolathlon (73.2% vs 53.4%).

But Luna is not designed to compete with K3 on intelligence. It is designed to be fast and cheap. At $1/$6 per million tokens, Luna costs roughly one-third of K3 on input and 2.5x less on output. It generates tokens at 130+ tok/s vs K3's 39.

The question is not "which model is smarter?" but "is the capability gap worth 2.5-3x more money?" For classification, summarization, extraction, routing, and high-volume batch work, Luna's lower scores are still strong enough and its speed and cost advantages are decisive. For coding, agentic tasks, and anything requiring frontier reasoning, K3 is the clear winner.

4. Sol Ultra mode: the ceiling K3 cannot match

Sol has one capability K3 does not: Ultra mode, which coordinates four parallel sub-agents working together on a single task.

The Ultra figures come from OpenAI's own launch reporting, and they cannot be merged with the numbers in the table above. Moonshot's model card has no Sol Ultra column at all, and the two vendors ran Terminal-Bench 2.1 on different harnesses. Taking each table on its own terms:

• OpenAI's reporting: Sol Ultra 91.9%, plain Sol 88.8%, GPT-5.5 88.0%, Claude Mythos 5 84.3%
• Moonshot's card: K3 88.3% on the Kimi Code harness, Claude Fable 5 88.0% on Terminus 2, Claude Opus 4.8 84.6% on Terminus 2

Read those as two separate leaderboards, not one ranking. Sol Ultra's 91.9% is the highest published figure on that benchmark from any vendor, but no independent evaluator has reproduced it and it has not been run against K3 on a common harness. OpenAI also reports Sol Ultra at 92.2% on BrowseComp, against K3's vendor-reported 91.2%, with the same caveat.

Ultra is not a separate model. It is a mode within Sol that spends more tokens for harder coordination. For the absolute hardest agentic problems, Sol Ultra represents a capability tier K3 cannot reach regardless of price.

Pricing across the full family

The pricing picture is more complex than it looks at first glance because K3's token efficiency works differently from the GPT-5.6 family.

Pricing from Moonshot and OpenAI. Cost per task from Artificial Analysis, July 2026.

Three pricing dynamics worth understanding:

K3 vs Sol: cheaper per token, closer per task. K3's $3/$15 looks dramatically cheaper than Sol's $5/$30. But MyClaw's analysis of Artificial Analysis data found K3 used roughly 130 million output tokens across the Intelligence Index evaluation vs Sol's ~70 million. K3's per-token savings are partially offset by nearly double the output volume. Cost per completed task is $0.94 for K3 and $1.04 for Sol. The gap narrows to roughly 10%.

K3 vs Terra: K3 costs more. Terra matches K3's $15 output rate and undercuts it on input ($2.50 vs $3.00). On a blended basis, Artificial Analysis measures Terra (high) at $0.34 per task vs K3's $0.94. Terra is nearly 3x cheaper per task while scoring 8 points lower on the Intelligence Index. For many production workloads, that tradeoff favors Terra.

Sol's long-context surcharge. Prompts exceeding 272K input tokens trigger a 2x input and 1.5x output multiplier on Sol. At 500K input tokens, Sol's effective input rate jumps to $10/M. K3's pricing is flat across its full 1M context. For long-context workloads specifically, K3's pricing advantage over Sol widens significantly.

Speed, reasoning controls, and context window

Speed

K3 is the slowest model in this comparison at 39 tok/s. Sol generates at roughly 53-63 tok/s depending on the provider and measurement window (Artificial Analysis reports 63, OpenRouter measures 37, CodingFleet reports 53). Terra and Luna both run at roughly 130 tok/s. For interactive use, all three GPT-5.6 tiers are noticeably faster than K3. Sol on Cerebras hardware can reach up to 750 tok/s for select customers, a latency play that no other model in this comparison can match.

K3's time-to-first-token (4.23s) is fast, reflecting a shorter reasoning phase before output begins. But its overall time-per-task (8.6 minutes on Artificial Analysis) is the longest because it generates more total output tokens.

Reasoning controls

Sol offers the widest range: none through max, plus Ultra mode. Terra and Luna support the same effort levels without Ultra. K3 exposes three: low, high, and max, with max as the default. Thinking cannot be disabled at any K3 setting.

At K3's launch this was the single biggest operational difference between the two. It is narrower now. The GPT-5.6 family still gives you finer granularity, a genuine off switch, and three separate models to route across, so you can send simple tasks to Luna at low effort and reserve Sol Ultra for the hardest problems. K3 gives you three settings on one model and always thinks. That still favours OpenAI on mixed workloads, but the advantage now rests on the tiered family rather than on K3 lacking any lever at all.

Context window

All four models accept roughly one million tokens of input. The GPT-5.6 family lists 1,050,000 tokens. K3 lists 1,048,576. The difference is negligible. All four cap output at 128K tokens, though K3 can be configured up to 1M.

K3 adds native video input alongside text and images. The GPT-5.6 family supports text and images but not video.

Open weights vs the OpenAI ecosystem

K3 published full weights on July 27, 2026 under the Kimi K3 License, a bespoke document rather than a standard open-source license. It reads like MIT for most of its length, then adds two commercial conditions. Resellers offering third parties inference or fine-tuning access need a separate agreement with Moonshot once combined licensee and affiliate revenue passes $20 million over any consecutive 12 months. Products above 100 million monthly active users or $20 million in monthly revenue must display "Kimi K3" in the interface. Internal use and access through certified inference partners are exempt.

Self-hosting 2.8 trillion parameters remains a real undertaking. Moonshot's own guidance recommends 64 or more accelerators, while SGLang's post-release serving recipes start at 8 GPUs on B300, GB300, or MI350-class silicon, 16 on B200, GB200, or H200, and 32 on H100. Open weights enable fine-tuning, air-gapped deployment, and data sovereignty, subject to those license triggers. For a broader look at Moonshot's model lineup, see Emergent's What is Kimi guide.

GPT-5.6 is closed and API-only across all three tiers. The ecosystem advantage is substantial: Codex CLI, web search, file search, computer use, ChatGPT integration, and a tiered pricing structure that lets you route by task complexity. For teams already building on OpenAI, the three-tier family eliminates the need to compare across vendors for different workload segments.

For teams evaluating Kimi K3 alternatives across both open-weight and closed models, Emergent's roundup covers six options spanning different price and capability tiers.

When to pick K3 vs Sol vs Terra vs Luna

Decision matrix based on Artificial Analysis benchmarks, LMArena results, OpenAI system card, and official pricing, July 2026.

The strongest architecture routes by task, not by model. K3 is most compelling as a frontend specialist, long-context workhorse, and open-weight option. The GPT-5.6 family is most compelling as a complete tiered system where you match cost to difficulty. If you use both, route at task boundaries rather than switching mid-session.

For a comparison of how GPT-5.6 compares to Claude's lineup, see GPT-5.6 vs Sonnet 5.

Beyond the model comparison

If you're not building AI infrastructure and just need a working app, there's a simpler path. Emergent lets you describe an application in plain language and get a production-ready, full-stack product with a real backend, real integrations like Stripe, MongoDB, and Shopify, and code you own. It runs Claude, OpenAI GPT, and Google Gemini under the hood through its Universal LLM Key, so you get frontier model capabilities without managing API keys or comparing token prices. Both GPT-5.6 Sol and Terra are already live on Emergent.

Skip the model comparisons and API setup. Describe your app and let Emergent handle the rest. Start Building and see how far a prompt gets you.