Eval Suite 3 · the running board

Leaderboard

Updated 2026-08-13 · 23 tasks · 12 models · 33 model×effort columns · 1,384 runs · v3 suite
How to read this One row per model at the effort level that scored best for it on this suite (ties break to the cheaper run; a partial-coverage sweep cannot outrank a full one). Whiskers on the cards are ±1 standard error, and on this suite that matters: ten of the twelve models sit within one stderr of the leader, so the order inside that band is not supported by the data. Cost and speed are where they actually separate. Every tested effort level is plotted in the effort-curve card below.
VulcanBench Eval Suite 3 rankings: Grok 4.5 at 90 percent pass at one, Claude Fable 5 at 89, DeepSeek V4-Flash at 88, then DeepSeek V4 Pro, GPT-5.6 Terra, Claude Opus 5, Grok 4.6, and GPT-5.6 Sol at 87, GPT-5.6 Luna 86, Qwen3.8-Max 81, Claude Haiku 4.5 76, Kimi K3 74
Rankings card. pass@1 by model at its best-scoring effort, whiskers ±1 stderr, with cost per task run and run count alongside.
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Eval Suite 3 · 23 tasks · ranked by pass@1
How it is measured →

pass@1 is the mean per-task success rate across all runs in the column. Repeat-swept models aggregate three or more attempts per task; single-pass columns (n≤23) carry wider uncertainty. Cost is total spend at list API prices ÷ runs in the column; time is sandbox wall-clock per task.

#Modelpass@1±1 se$/task runmin/taskRuns
1Grok 4.5 highLeader89.9%6.1$0.478.450
2Claude Fable 5 low · 19/23 tasks*89.5%7.2$0.513.519
3DeepSeek V4-Flash maxCheapest88.4%6.2$0.0611.268
4DeepSeek V4 Pro high87.0%6.2$0.117.469
5GPT-5.6 Terra mediumFastest87.0%7.2$0.192.369
6Claude Opus 5 low · Report 10†87.0%7.2$0.615.223
7Grok 4.6 medium87.0%7.2$0.6914.223
8GPT-5.6 Sol high87.0%7.2$0.694.223
9GPT-5.6 Luna high85.5%6.6$0.173.869
10Qwen3.8-Max low81.2%7.8$0.6119.669
11Claude Haiku 4.5 default · 21/23 tasks*76.2%9.5$0.425.321
12Kimi K3 extra-high · 19/23 tasks*73.7%10.4$0.6917.219

* Partial task coverage: Claude Fable 5 excludes tasks refused by its safety filters (19/23 at low); Kimi K3 19/23; Claude Haiku 4.5 21/23. † Claude Opus 5 rows come from Report 10 (single runs, 2026-07-26). Effort names are each provider’s own enum: DeepSeek’s top level is “max”, Qwen’s and Grok 4.6’s is “xhigh”. Claude Haiku 4.5 and Kimi K3 have a single tested setting.

Wall-clock minutes per task, fastest first.

Speed card: GPT-5.6 Terra finishes a task in 2.3 minutes on average, GPT-5.6 Luna 3.8, Claude Fable 5 3.5, GPT-5.6 Sol 4.2, Claude Opus 5 5.2, Claude Haiku 4.5 5.3, DeepSeek V4 Pro 7.4, Grok 4.5 8.4, DeepSeek V4-Flash 11.2, Grok 4.6 14.2, Kimi K3 17.2, Qwen3.8-Max 19.6
Speed card. Average sandbox wall-clock per task run at each model’s best-scoring effort; pass@1 alongside for context.

API spend per task run, lowest first.

Cost card: DeepSeek V4-Flash at 6 cents per task run, DeepSeek V4 Pro 11 cents, GPT-5.6 Luna 17 cents, GPT-5.6 Terra 19 cents, Claude Haiku 4.5 42 cents, Grok 4.5 47 cents, Claude Fable 5 51 cents, Claude Opus 5 and Qwen3.8-Max 61 cents, Grok 4.6, GPT-5.6 Sol and Kimi K3 69 cents
Cost card. Total list-price API spend for the column divided by runs. Negotiated, cached-input, and batch rates all differ; treat these as relative, not quotes.

What happens when you turn the reasoning knob.

The rows above show one effort level per model. This card shows every level that was tested, on one shared y-scale: more reasoning is not reliably better on this suite, and for several models it is worse.

Effort curves: pass@1 at each reasoning-effort level for nine swept models. Grok 4.5, DeepSeek V4-Flash, GPT-5.6 Sol, and GPT-5.6 Luna rise with effort; GPT-5.6 Terra and Grok 4.6 peak mid-ladder; Claude Fable 5 dips at medium; Claude Opus 5 and Qwen3.8-Max fall as effort rises
Effort-curve card. Each x-axis is that provider’s own documented enum, not a common scale. Larger point = the model’s best level.

VulcanBench Eval Suite 3: 23 tasks from real merged post-cutoff PRs (Python 9, TypeScript 4, Rust 4, Go 3, JavaScript 3), graded by deterministic hidden tests in Docker with model judges disabled. Each row aggregates every fresh run of that model at that effort whose task hashes match the frozen suite; pass@1 is the mean per-task success rate and its standard error is the spread of per-task means. The board is regenerated from the public run data with the harness’s scripts/rankings-chart pipeline; nothing here is edited by hand. Individual reports on the Benchmarks page carry the per-task detail, failure modes, and caveats behind each row.