Claude Opus 4.8
The most capable model for complex planning and orchestration
Compare live model rankings from real Kilo Code usage. See which models developers choose for coding, planning, debugging, and agent workflows across 500+ hosted options.
Our picks based on real-world testing • View usage stats
The most capable model for complex planning and orchestration
Built for agentic engineering — 256k context, no output limits
Remarkably detailed and consistent across modes
Frontier-class coding model from NVIDIA. Currently free in Kilo.
Cost vs performance across the most capable coding models
| Rank | Model | Completion | Cost per attempt |
|---|---|---|---|
| 1 | 74.2% | $72.63 | |
| 2 | 70.1% | $100.51 | |
| 3 | 67.6% | $85.19 | |
| 4 | 64.7% | $104.49 | |
| 5 | 55.1% | $53.37 | |
| 6 | 54.4% | $24.84 | |
| 7 | 50.6% | $30.70 | |
| 8 | 49.4% | $23.98 | |
| 9 | 47.6% | $4.92 | |
| 10 | 47.6% | $10.35 |
Official Kilo eval results on Terminal Bench 2.0. Cost and token usage are averaged per complete benchmark attempt.
See which models lead in Code, Plan, Debug, Ask, and Orchestrator
| Rank | Model | Usage |
|---|---|---|
| 01 | 37.4% | |
| 02 | step-3.7-flash | 26.4% |
| 03 | owl-alpha | 5.9% |
| 04 | 4.3% | |
| 05 | laguna-xs.2 | 3.9% |
| 06 | 3.8% | |
| 07 | 2.1% | |
| 08 | 2.0% | |
| 09 | 1.9% | |
| 10 | 1.7% |
| Rank | Model | Usage |
|---|---|---|
| 01 | 28.3% | |
| 02 | step-3.7-flash | 17.9% |
| 03 | 8.0% | |
| 04 | 5.6% | |
| 05 | 4.7% | |
| 06 | owl-alpha | 4.6% |
| 07 | 4.0% | |
| 08 | 3.3% | |
| 09 | laguna-xs.2 | 3.0% |
| 10 | 2.9% |
| Rank | Model | Usage |
|---|---|---|
| 01 | 32.5% | |
| 02 | step-3.7-flash | 21.9% |
| 03 | owl-alpha | 5.6% |
| 04 | 4.9% | |
| 05 | 4.8% | |
| 06 | 4.6% | |
| 07 | laguna-xs.2 | 4.4% |
| 08 | 4.3% | |
| 09 | 2.8% | |
| 10 | 1.7% |
| Rank | Model | Usage |
|---|---|---|
| 01 | 39.3% | |
| 02 | step-3.7-flash | 20.8% |
| 03 | 5.8% | |
| 04 | owl-alpha | 4.4% |
| 05 | 4.3% | |
| 06 | laguna-xs.2 | 3.2% |
| 07 | 2.6% | |
| 08 | 2.5% | |
| 09 | 1.9% | |
| 10 | 1.9% |
| Rank | Model | Usage |
|---|---|---|
| 01 | step-3.7-flash | 37.6% |
| 02 | 29.7% | |
| 03 | 6.4% | |
| 04 | nex-n2-pro | 4.6% |
| 05 | 3.0% | |
| 06 | 2.7% | |
| 07 | 2.1% | |
| 08 | 1.9% | |
| 09 | 1.6% | |
| 10 | 1.6% |
| Rank | Model | Usage |
|---|---|---|
| 01 | 33.0% | |
| 02 | step-3.7-flash | 14.7% |
| 03 | 11.1% | |
| 04 | 8.9% | |
| 05 | 6.0% | |
| 06 | 5.8% | |
| 07 | 2.9% | |
| 08 | 2.6% | |
| 09 | 2.0% | |
| 10 | 1.7% |
Most-used models across Kilo Code in the last 24 hours
| Rank | Model | Usage |
|---|---|---|
| 01 | 69.6B | |
| 02 | 68.7B | |
| 03 | step-3.7-flash | 52.9B |
| 04 | 45.3B | |
| 05 | 25.6B | |
| 06 | 22.7B | |
| 07 | ling | 17.6B |
| 08 | 16.5B | |
| 09 | owl-alpha | 16.3B |
| 10 | 16.2B |
Token usage by model over time, stacked daily
Browse and compare all available AI coding models
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GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....
Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...
Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...
Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....
Hy3 preview is a high-efficiency Mixture-of-Experts model from Tencent designed for agentic workflows and production use. It supports configurable reasoning levels across disabled, low, and high modes, allowing it to...
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MiMo-V2-Pro is Xiaomi's flagship foundation model, featuring over 1T total parameters and a 1M context length, deeply optimized for agentic scenarios. It is highly adaptable to general agent frameworks like...
MiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro....
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex...
As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...
Methodology
Synthetic benchmarks measure one capability at one moment. This leaderboard measures what developers come back to: real coding work, long planning sessions, debugging, review, and agentic tasks across 500+ models.
The rankings reflect real token usage by Kilo Code developers, not synthetic benchmarks.
Use the Top Models by Mode section to see which models lead in Code, Plan, Debug, Ask, and Orchestrator.
Each model links to a dedicated page with benchmark scores, pricing, context length, and speed data.
All 500+ models are available in Kilo Code. Switch from the model selector at any time.
The Kilo Code leaderboard shows live rankings of AI coding models based on real token usage by 3M+ developers. Rankings reflect genuine developer preference and update every 5 minutes.
Models are ranked by total token usage from Kilo Code developers. The ranking reflects real-world developer preference and can be filtered by modes such as Code, Plan, Debug, Ask, and Orchestrator.
The current top-ranked model is Poolside: Laguna M.1 (free), based on real developer usage. The best AI model for your workflow can vary by coding, planning, debugging, and agent tasks, so compare the live rankings by mode before choosing.
The leaderboard updates every 5 minutes with fresh usage data from real Kilo Code developers.