Guide
Best AI models for Roblox development
Updated November 5, 2026 - based on Roblox OpenGameEval benchmarks
Not all AI models perform equally on Roblox tasks. Roblox's own OpenGameEval benchmark tests models on real Studio tasks — from editing scripts to building entire game systems — and the results vary widely. The benchmark now covers 117 evaluations across two categories: 87 code generation evals and 30 debug evals.
Code generation leaderboard (87 evals)
The main leaderboard tests writing and modifying Luau scripts across a range of task complexity:
| Model | Pass@1 | Pass@5 | Cons@5 | All@5 | Tool err |
|---|---|---|---|---|---|
| Claude Fable 5 | 50.34% | 62.07% | 51.09% | 39.52% | 1.40% |
| Claude Opus 4.6 | 48.05% | 59.77% | 48.05% | 38.28% | 0.71% |
| Gemini 3.5 Flash | 48.05% | 63.22% | 49.03% | 33.86% | 3.30% |
| Gemini 3 Flash Preview | 47.82% | 60.92% | 48.84% | 35.12% | 5.51% |
| Claude Opus 4.7 | 43.45% | 58.62% | 43.45% | 32.18% | 1.33% |
| GPT-5.5 (Reasoning: M) | 40.69% | 56.32% | 40.13% | 30.62% | 0.91% |
| GPT-5.4 (Reasoning: M) | 40.23% | 55.17% | 40.00% | 29.02% | 1.81% |
Debug leaderboard (30 evals)
Debug evals present a buggy script and ask the model to find and fix it. A different set of models has been evaluated here:
| Model | Pass@1 | Pass@5 | Cons@5 | All@5 | Tool err |
|---|---|---|---|---|---|
| Claude Fable 5 | 64.67% | 73.33% | 66.09% | 54.66% | 1.01% |
| Gemini 3.1 Pro | 56.67% | 70.00% | 58.36% | 42.68% | 5.97% |
| GLM 5 | 56.00% | 73.33% | 59.87% | 33.98% | 2.39% |
| Claude Opus 4.7 | 52.67% | 63.33% | 53.14% | 43.57% | 4.26% |
| GPT-5.4 (Reasoning: M) | 51.33% | 63.33% | 52.08% | 39.70% | 2.98% |
| Gemini 3 Flash Preview | 51.33% | 63.33% | 51.06% | 43.31% | 4.58% |
| Claude Opus 4.6 | 50.67% | 66.67% | 49.52% | 40.85% | 0.96% |
| GPT-5.5 (Reasoning: M) | 50.00% | 66.67% | 51.02% | 35.18% | 1.54% |
| Gemini 3.5 Flash | 49.33% | 70.00% | 48.46% | 36.33% | 3.37% |
| GPT Codex 5.3 | 47.33% | 70.00% | 47.90% | 27.00% | 3.21% |
| Claude Sonnet 4.6 | 46.00% | 60.00% | 46.47% | 33.87% | 6.47% |
Full leaderboard and detailed model reviews on GitHub.
Score freshness: Roblox last published these leaderboard results on June 10, 2026. The repository has kept moving since then — three eval checks corrected on July 24, one code generation eval removed on July 30, and a Studio Eval Runner plugin added on August 28 — but no revised model run has been published. Read the tables as a June snapshot. See our benchmark methodology update.
Update, November 5, 2026: Roblox has since published a separate run in its OpenGameEval paper (October 1, 2026): 13 models, 84 core tasks, 16 attempts per task. Claude Fable 5 again has the best single-attempt rate (51.7%) and five-of-five rate (39.4%), but the top five — Fable 5, Gemini 3 Flash, Claude Opus 4.8, Gemini 3.1 Pro (51.3%), and Gemini 3.5 Flash — sit within 1.1 points of each other. The leaderboard tables above have not changed.
What do the scores mean?
- Pass@1 — success rate on the first attempt. The most important metric for interactive use where you want the AI to get it right immediately.
- Pass@5 — success rate within 5 attempts. Shows how reliable a model is when you let it retry or iterate.
- Cons@5 — success in at least 3 out of 5 attempts. A measure of consistency: a high Cons@5 means the model gets it right most of the time, not just occasionally.
- All@5 — success in all 5 attempts. The ceiling metric: how reliably a model can solve a task every single time, with no variance. Claude Fable 5 leads both sets: 39.52% on code generation and 54.66% on debug.
- Tool error rate — how often the model makes a malformed tool call. Lower is better. High tool error rates slow sessions down and burn tokens on retries.
Recommendations by use case
Best overall: Claude Fable 5
#1 on both leaderboards. On code generation it is the first model to cross 50% Pass@1 (50.34%) on the harder 87-eval set, and it leads consistency on both Cons@5 (51.09%) and All@5 (39.52%). On Debug it is in a class of its own: 64.67% Pass@1, 8 points ahead of the next model, with 54.66% All@5. Its tool error rates (1.40% codegen, 1.01% debug) are among the lowest tested. If you pick one model for everything, pick this one.
Best tool-call precision: Claude Opus 4.6
Second on code generation Pass@1 (48.05%) with the lowest tool error rate of any model tested on both sets (0.71% codegen, 0.96% debug). When you need clean, well-formed edits with no malformed tool calls or retry overhead, Opus 4.6 is the most precise choice.
Best Pass@5 / iterative use: Gemini 3.5 Flash
Leads all models on Pass@5 (63.22%), ahead of even Fable 5 (62.07%). If you work iteratively — running the same prompt a few times and taking the best result — Gemini 3.5 Flash gives you the best odds. Its higher tool error rate (3.30%) is the tradeoff.
Best for debugging: Claude Fable 5, then Gemini 3.1 Pro
Fable 5 leads the Debug leaderboard at 64.67% Pass@1. Gemini 3.1 Pro (56.67%) is the strongest non-Anthropic option for reading existing (broken) code, diagnosing the root cause, and applying a targeted fix. Note: Gemini 3.1 Pro has not yet been evaluated on the 87-eval code generation set.
Best efficiency: Claude Opus 4.7
Opus 4.7's Pass@1 (43.45%) is 4.6 points below 4.6 (48.05%), but the gap is not statistically significant (p=0.24) on this eval suite. Where it stands out is tool call efficiency: 39% fewer tool calls per task, with the largest drops in exploration tools. For large projects where you pay per token, Opus 4.7 completes tasks faster and cheaper with comparable accuracy on well-specified tasks.
Best budget option for debugging: GLM 5
Third on Debug Pass@1 (56.00%) and tied with Fable 5 for the best Debug Pass@5 (73.33%), but not yet evaluated on the main code generation set. Strong choice for debugging workflows at lower cost than the Claude and Gemini Pro tier.
Models released since the last run
Several strong models shipped after Roblox published these tables, so their absence here is not a verdict. The most significant is GPT-6 Astra, released September 3, 2026. In its launch write-up, Artificial Analysis scored it level with Claude Fable 5.1 in Claude Code on its Coding Agent Index, and its hallucination rate at max effort fell from 92% to 51%. That second number matters for an agent driving Studio through MCP, where an invented API name costs a whole turn. Astra has no OpenGameEval result, and it is not in Roblox's October paper either. The independent CEGame-Bench (October 19, 2026) did rank GPT-6 Astra in Codex CLI first on its 10-task Roblox Studio track, but that track builds whole games from an empty project, a different job from OpenGameEval's edits to existing places.
Resist mapping general coding scores onto this page. OpenGameEval rewards a narrower skill: reading a live scene tree, making a small edit, and verifying it. Models have repeatedly placed differently here than their SWE-bench-style results predict. Until a run covers the model you care about, the honest approach is to test a new model on your own game with a bounded task and a stated check.
Recent creator demos make the distinction concrete. Compare the copyable Opus 5.5 Roblox Crossroads map with the GPT-6 Astra kart racer prompt. Both are useful examples of AI-assisted building, while neither measures repeatable model performance on OpenGameEval tasks.
Using multiple models
In BloxBot, you can switch models per-session. A practical workflow:
- Use Claude Fable 5 as the default for building features and debugging existing scripts — it leads both leaderboards
- Use Gemini 3.5 Flash for rapid prototyping and exploration where you're iterating quickly
- Switch to Claude Opus 4.6 for precise edits where malformed tool calls are costly
- Use Claude Opus 4.7 for well-specified tasks on large projects where session length and cost matter
BloxBot lets you switch between supported providers without changing the Studio connection. Model availability depends on your provider and BloxBot version.