Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing

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Published 7/26/2026

Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing

Sol wins the single shot; Kimi K3 casts the wider net at ~64% less per solved task, and routing between them beats either alone.

Authors

Zain Hasan, Shobhit Dixit

Table of contents

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Links in this article

Kimi K3 K3 vs. Fable ROI calculator

Summary

GPT-5.6 Sol edges Kimi K3 on single-shot quality, but Kimi wins on pass@k with k > 1 and costs 64% less per completed task. The two models succeed and fail in different ways, which makes routing between them the strongest play on the benchmark. Kimi K3 vs GPT-5.6 Sol is close on DeepSWE pass@1: Sol leads 72.7% to 68.5%, a 4.2 point gap. Give the models more attempts and Kimi K3 pulls ahead. It wins pass@2 (82.0 vs 81.0) and pass@4 (89.4% vs 85.8%). Kimi K3 is far cheaper: \$4.65 per rollout vs \$8.37, and 2.8x more solved tasks per dollar. GPT-5.6 Sol is the more reliable model: it solves more tasks on all 4/4 tries (61 vs 45). The models diverge (0.46 correlation across which tasks they solve correctly and incorrectly) and fail differently, so a Kimi-first cascade that escalates to Sol covers 108 of 113 tasks and reaches about 85.6%.

DeepSWE · Head to Head

Kimi K3 vs GPT-5.6 Sol at a glance

Metric Kimi K3 GPT-5.6 Sol

DeepSWE pass@1 68.5% 72.7%

DeepSWE pass@2 82.0% 81.0%

DeepSWE pass@4 89.4% 85.8%

Coverage (solved at least once) 89.4% 85.8%

Reliability (4/4 pass rate) 76.6% 84.5%

Tasks solved four-for-four 45 61

Cost per rollout $4.65 $8.37

Solved tasks per $100 14.7 5.3

Median rollout time 66 min 17 min

Open weights Yes No

In our Kimi K3 vs GPT-5.6 Sol comparison on DeepSWE, a benchmark that tests a model's software engineering ability across many task types and programming languages, the story is not who sits at the top of the leaderboard. GPT-5.6 Sol took the pass@1 crown two weeks ago with 72.7% under DeepSWE's official scoring, the strongest single shot and the highest reliability ever recorded on the benchmark. Kimi K3, the open-weight newcomer, answers on the other axis: 89.4% pass@4, better than every flagship on the board, Sol included, while costing \$4.65 a rollout to Sol's \$8.37. We analyzed all 904 graded rollouts (113 tasks, four trials each, at max effort on both sides) from the published per-trial records at deepswe.datacurve.ai. The DeepSWE scoreboard: pass@1 and pass@k Given a single attempt, Sol wins clearly: 72.7% to 68.5% under DeepSWE's official scoring. But the gap closes fast. At two attempts Kimi K3 is already ahead, 82.0 to 81.0. At four attempts, Kimi's 89.4% beats Sol's 85.8% by 3.6 points, the best pass@4 of any flagship-tier config on the board.

Cost comparison: Kimi K3 vs GPT-5.6 Sol pricing Kimi K3: \$4.65 per rollout. GPT-5.6 Sol: \$8.37. Per solved task, Kimi delivers 14.7 solves per \$100 versus Sol's 5.3, about 2.8x the work per dollar. The tradeoff is speed and steps: a median Sol rollout takes 17 minutes against Kimi's 66 (using the Kimi K3 API from Moonshot as of July 25th), with Sol using roughly 40% fewer steps. End-to-end latency should improve as inference providers optimize Kimi K3, though the extra steps are a model-behavior trait.

Kimi K3 vs GPT-5.6 Sol by programming language The two are tied on Go (79 each). Sol leads Python (74-68), TypeScript (66-60), and JavaScript (75-65). Kimi K3 takes Rust (65-60).

Coverage vs reliability: opposite corners of the plane Decompose pass@1 and pass@4 into coverage (tasks solved at least once across four tries) and reliability (tasks solved on all four), and the two models sit in opposite corners. Sol posts 84.5% reliability with 61 tasks solved four-for-four, across a modest 85.8% of the benchmark. Kimi K3 reaches 89.4% coverage, wider than any flagship, but only 76.6% reliability and 45 rock-solid tasks. Sol is steadier and more deterministic; Kimi K3 casts the wider net.

How different are Kimi K3 and GPT-5.6 Sol? Where Kimi K3 versus Fable was essentially an echo chamber (0.72 correlation), Kimi K3 and Sol diverge: per-task correlation is 0.46, with four-for-four-versus-zero-for-four corners in both directions. The models succeed and fail in genuinely different ways. That is exactly what makes them a strong pair to route and cascade between: together they cover 108 of 113 tasks (95.6%), the best two-model portfolio on the benchmark.

How high can routing between them get you? Because the two disagree on 18 of 113 tasks, routing turns that disagreement into free accuracy. How much you capture depends on whether you have a verifier (your test suite) to catch a bad answer and escalate. The practical answer is about 85.6%: run Kimi K3 first and escalate to Sol only when the test suite rejects the result. That beats either model alone and even a perfect one-shot router (83.4%), because the cascade gives hard tasks two independent attempts instead of committing to one. It is also cheaper per solve than Sol alone, since Kimi K3 clears roughly 70% of the queue before Sol is ever invoked. The verifier does the heavy lifting: without one, a realistic router lands in the 70s and 83.4% is the ceiling.

DeepSWE · Routing strategies

Routing strategy: accuracy vs cost per task

Routing strategy Accuracy Cost / task

Best single model, no routing (Sol) 72.7% $8.37

Realistic learned router (1 shot) 72–83% ~$6

Perfect oracle router (1 shot, pick right model) 83.4% ~$6.07

Cascade: Kimi → Sol on failure 85.6% $7.30

Both models once, keep either 85.6% $13.02

The hard ceiling for this pair is 95.6%. Five of the 113 tasks are solved by neither model across all eight combined attempts. Past 95.6% you need a third model, not a better router.

Where each wins, by task type To route well, classify tasks by what the coding ask actually is. Sol leads 5 of 8 domains, Kimi K3. Send serialization (92-79), concurrency (72-55), and program analysis (64-56) to Sol. Send ops tooling (79-73) and...