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Harun’s October 6, 2026 post says his KODA AI coding ecosystem scored 52.2% against Claude Code’s 48.2% in a max-effort Code Index comparison. The same post reports Claude Code narrowly ahead at medium effort, 46.9% to KODA’s 46.3%. These are the author’s results, not independently verified benchmark standings: the reviewed sources do not establish the run configuration or provide reproducible artifacts.
What the reported comparison says
Harun describes comparing KODA with KiloCode, Claude Code, and OpenCode using DeepSeek V4 Flash across the harnesses. The figures below are the results reported in his post; they should not be read as official Code Index rankings or broad measures of how these tools perform in other settings.
| Tool | Max effort | Medium effort |
|---|---|---|
| KODA | 52.2% (Harun’s reported experiment) | 46.3% (Harun’s reported experiment) |
| Claude Code | 48.2% (Harun’s reported experiment) | 46.9% (Harun’s reported experiment) |
| KiloCode | 49.5% (Harun’s reported experiment) | not stated (Harun’s post) |
| OpenCode | 47.6% (Harun’s reported experiment) | not stated (Harun’s post) |
The reported ordering changes with the effort setting: KODA leads the max-effort figures shown, while Claude Code leads the medium-effort figures by 0.6 percentage points. That is a description of this post’s table, not evidence that either system generally outperforms the other.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe Code Index maintains a separate benchmark page. Its existence provides benchmark context but does not verify Harun’s particular comparison.
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How Harun says he built KODA
In the post, Harun describes KODA as an ecosystem with three parts:
- A 92 KB single-file web app.
- A roughly 22 KB code editor extension.
- A Cloudflare edge-worker harness that routes among three AI models.
He identifies GPT-OSS-120B as the general-purpose model, GPT-OSS-20B as a faster fallback, and a DeepSeek R1 distill model as a reasoning specialist. Those are descriptions in the post; the components and their operation were not independently inspected.
Rank #2
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Harun says he built the system on a Poco C55, which he describes as a $150 phone, and emphasizes the device’s 4 GB of RAM. The price is his account of the phone, not a current market price, and the post does not establish that the setup is representative of other phones or development workflows.
What the post’s other performance numbers mean
The post includes model and system figures beyond the KODA comparison. They are claims reported by Harun, with attribution in his article, rather than independently validated results here.
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GPT-OSS-120B
Harun lists 62.4% on SWE-bench Verified, attributed in his post to a Groq model card; 26% in a leaderboard harness; 45.9% in a high-reasoning, bash-only setup; 90.0% MMLU; 81.3% multilingual MMLU; and approximately 500 tokens per second.
GPT-OSS-20B
For this model, the post reports 60.4% SWE-bench Verified at high effort and 53.3% at medium effort, 91.7% on AIME 2025 with tools and 72.1% without tools, and a score of 40.7 on the Artificial Analysis Coding Index.
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DeepSeek R1 distill
The post lists 86.0% AIME 2024 pass@1, 76.3% AIME 2025 pass@1, 61.5% HMMT February 2025, 61.1% GPQA Diamond, 60.5% LiveCodeBench, 49.2% SWE-bench Verified resolved, and 53.3% Aider Polyglot accuracy. The DeepSeek V4 Flash model card has its own benchmark tables; those tables do not independently confirm KODA’s experiment or the post’s figures for other models.
Implementation and stress-test claims
Harun also claims first-token latency under 500 ms, 54/54 senior-level stress tests, and performance budgets for safety and prompt-injection filtering. The post’s reviewed material does not independently establish how these measurements were conducted, so they should be treated as author-reported system claims rather than reproducible findings.
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What would make the comparison reproducible
A percentage is useful only in the context of the benchmark tasks, scoring rules, and conditions under which it was produced. The reviewed material does not establish the details needed to reproduce Harun’s KODA-versus-tool run. A useful report would include:
- The Code Index version and exact task set used.
- The exact model and harness versions, including the configuration of each tool.
- Effort settings, agent prompts, and which tools each agent could use.
- The number of trials, scoring method, and treatment of failures or incomplete runs.
- Logs or other artifacts that let readers inspect and repeat the runs.
Without these details, the figures are best read as an intriguing account of one reported experiment—not a verified general ranking. The post’s account of building an AI coding setup on a constrained phone is also distinct from the question of whether its benchmark result will reproduce under controlled conditions.
Quick Recap
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