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.
High volume, schemas, low complexity. Reliability + cheap.
Best value
gemma-3-4b-it
Best quality
mimo-v2.6-pro
Filter: Blended price ≤ $2/M, quality ≥ 1250. Sorted by value. These workloads run at scale; small price diffs are real money. 116 models survived.
| Model | Quality | Ctx | In /1M | Out /1M | Value ↓ | |
|---|---|---|---|---|---|---|
| 01 | gemma-3-4b-itvalueGoogle | 1290.8 | 131k | $0.05 | $0.10 | 342123.1 |
| 02 | granite-4.1-8bIBM | 1290.4 | 131k | $0.05 | $0.10 | 341670.8 |
| 03 | gpt-oss-20bOpenAI | 1287.6 | 131k | $0.02 | $0.11 | 336039.4 |
| 04 | llama-4-scout-17b-16e-instructMeta | 1279.4 | 16k | $0.05 | $0.10 | 328682.2 |
| 05 | gemma-3n-e4b-itGoogle | 1305.5 | 33k | $0.06 | $0.12 | 299503.1 |
| 06 | glm-5.3-flashZ.ai | 1469.6 | 1.0M | $0.06 | $0.20 | 297230.2 |
| 07 | gemma-3-12b-itGoogle | 1334.2 | 131k | $0.05 | $0.15 | 278518.4 |
| 08 | qwen3-30b-a3b-instruct-2507Alibaba | 1384.1 | 262k | $0.05 | $0.19 | 256774.2 |
| 09 | gpt-5-nano-highOpenAI | 1320.0 | 400k | $0.03 | $0.20 | 216919.9 |
| 10 | mimo-v2.6-flashXiaomi | 1456.4 | 1.0M | $0.14 | $0.28 | 191781.2 |
| 11 | nvidia-nemotron-3-nano-30b-a3b-bf16Nvidia | 1347.7 | 262k | $0.06 | $0.24 | 186931.0 |
| 12 | mimo-v2.5Xiaomi | 1427.5 | 1.1M | $0.14 | $0.28 | 179624.7 |
| 13 | mimo-v2.6-proqualityXiaomi | 1491.0 | 1.1M | $0.43 | $0.87 | 66401.8 |
| 14 | ernie-5.1Baidu | 1468.2 | 119k | $0.56 | $2.54 | 24085.7 |
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.