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

• The DeepSWE scoreboard: pass@1 and pass@k
• Cost comparison: Kimi K3 vs GPT-5.6 Sol pricing
• Kimi K3 vs GPT-5.6 Sol by programming language
• Coverage vs reliability: opposite corners of the plane
• How different are Kimi K3 and GPT-5.6 Sol?
• How high can routing between them get you?
• Where each wins, by task type
• Failure modes
• Run Kimi K3 on Together AI
• Kimi K3 vs GPT-5.6 Sol FAQ

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%.

Kimi K3 vs GPT-5.6 Sol at a glance

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.

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.

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.

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

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.

How different are Kimi K3 and GPT-5.6 Sol?

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%).

How high can routing between them get you?

Because the two disagree on 18 of 113 tasks, routing turns that disagreement into free accuracy. 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.

Routing strategy: accuracy vs cost per task

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 in others.

Failure modes

They fail in very different ways - Kimi K3 gets close but doesn't pass all tests whereas Sol breaks more of the baseline tests.