Tuesday, 6 October 2026
In the last 24 hours we dispatched 1,556 tasks across 4 models. Here's what we picked, and why.
An autonomous AI fleet, written in TypeScript, picks a model per task using a complexity router. No vibes, no PR team. This is the actual production output of that router:
| Model | Dispatches | Share | Why this one | |
|---|---|---|---|---|
| 01 | claude-sonnet-4-6 | 1,066 | 68.5% | implementation (standard) |
| 02 | claude-haiku-4-5 | 278 | 17.9% | implementation (light) |
| 03 | gpt-5.4-mini | 189 | 12.1% | implementation (codex pool) |
| 04 | claude-opus-4-6 | 23 | 1.5% | implementation (high complexity) |
Window: 24h to 2026-05-16T00:00:00Z. Source: daemon routing logs. The router writes a decision per dispatch; we parsed 1556 of them.
Filtered from arena.ai's leaderboard plus published API prices. Filter thresholds are listed under each tab; arguable. Treat this as a starting shortlist, not a verdict.
Complex multi-step problems. Where to send the gnarly stuff.
Best value
glm-5.3-flash
Best quality
gemini-4-argon-high
Filter: Math leaderboard, quality ≥ 1400. Sorted by quality. Use the cheaper end of this list as a router when you're sure the task is hard. 153 models survived.
| Model | Quality | Ctx | In /1M | Out /1M | Score ↓ | |
|---|---|---|---|---|---|---|
| 01 | gemini-4-argon-highqualityGoogle | 1536.0 | 1.0M | $2.00 | $10.00 | 1536.0 |
| 02 | claude-opus-5-maxAnthropic | 1530.8 | 1.0M | $5.00 | $25.00 | 1530.8 |
| 03 | claude-opus-5-highAnthropic | 1530.6 | 1.0M | $5.00 | $25.00 | 1530.6 |
| 04 | gemini-3.8-flash-highGoogle | 1523.2 | 1.0M | $0.75 | $3.75 | 1523.2 |
| 05 | claude-fable-5.1-maxAnthropic | 1520.5 | 1.0M | $10.00 | $50.00 | 1520.5 |
| 06 | claude-fable-5-highAnthropic | 1517.9 | 1.0M | $10.00 | $50.00 | 1517.9 |
| 07 | claude-opus-4-6-highAnthropic | 1517.3 | 1.0M | $5.00 | $25.00 | 1517.3 |
| 08 | claude-opus-5.5-highAnthropic | 1515.7 | 1.0M | $4.00 | $20.00 | 1515.7 |
| 09 | claude-opus-4-6Anthropic | 1512.2 | 1.0M | $5.00 | $25.00 | 1512.2 |
| 10 | gemini-3.7-flash-highGoogle | 1507.1 | 1.0M | $0.75 | $3.75 | 1507.1 |
| 11 | gemini-3.6-flash-highGoogle | 1504.2 | 1.0M | $0.75 | $3.75 | 1504.2 |
| 12 | gemini-3.5-flash-highGoogle | 1503.6 | 1.0M | $0.75 | $4.50 | 1503.6 |
Right now this is filter-on-arena.ai plus a public log of what we ran. Arena Elo measures pairwise human preference on short prompts. It does not measure: whether a model produces valid JSON under a schema, whether it hallucinates function names, whether it refuses queries it shouldn't, latency p99, rate-limit behaviour. Production teams need those signals.
We're building a benchmark runner — fixed prompt suites for RAG, structured extraction, code refactoring, function calling — run daily against every model. Raw inputs, outputs, judge rationale, costs published. When that lands, the "picks" section gets its real backing. Until then, the picks section is opinion with a citation, not measurement.