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Local models, the AI job market, and the frontier AI APIs at work - our current research tracks:
One benchmarked on our own hardware, one read from live data, one priced live from the routing market. Each links through to the page that owns the depth, and we're explicit about which numbers are ours and which we're reporting.
We benchmarked twelve local LLMs on a £6,200, 96GB rig. Five "rules" didn't survive it.
Five days, twelve open-weight models, one pinned engine, on two modded RTX 4090s - 48GB each, 96GB across the pair. Every number is measured on our own hardware, and the ones that mattered overturned rules we'd written into our own notes as fact: a 117-billion-parameter model serving at 153 tokens per second on consumer silicon, tensor parallelism 42% faster on a model that fits one card, the cards losing nothing when we capped their power, and a 16.6GB model that wouldn't load on a 48GB card because it ran a kilobyte short of a memory no spec sheet mentions. The full twelve-model table, the per-model flags and the raw dataset are downloadable from the hub.
AI isn't coming for your job. The data says it's coming for the grunt work.
27,173 live job ads from the YubHub index, joined to Anthropic's Economic Index and cross-checked against LinkedIn's Work Change Report. Three findings hold up everywhere we look: the most AI-exposed roles are still being hired in volume (augmentation in the data, not displacement); the work that stays is judgement, strategy and integration; and the market is rotating rather than retreating - 1.3 million new AI-related roles in two years, with job titles appearing weekly that didn't exist as categories three years ago.
The frontier scorecard, and where each model earns its tokens.
The local fleet exists to take work off paid APIs, and that decision needs the other half of the equation: what the frontier APIs cost and deliver. The prices below are live - pulled from OpenRouter's public model list when this page loads - while the benchmark scores and one-line reads are reported from public leaderboards, not yet our own gauntlet, and we label the split on purpose. The routing conclusion at the bottom is ours.
| Model | $/Mtok in / out | SWE-bench Verified | The one-line read |
|---|---|---|---|
| Claude Fable 5 | $10 / $50 | 95.0% | The long-horizon king; the tool-calling reliability benchmark |
| Claude Opus 4.8 | $5 / $25 | 88.6% | The reliable workhorse at half Fable's price |
| Kimi K3 | $3 / $15 | 93.4% | Vision in the agentic loop - fixes CSS from screenshots |
| DeepSeek V4 Pro | $1.6 / $3.2 | 80.6% | The economics-breaker: ~80% SWE-bench at ~28x cheaper output than Opus |
| Qwen Coder (latest) | $0.3 / $1 | - | The open-weight coder line we run locally, hosted for when the rig is busy |
| Model | $/Mtok in / out | Context | The one-line read |
|---|---|---|---|
| Claude Opus 4.8 | $5 / $25 | 1M | Orchestration, architecture, hard debugging - anywhere wrong costs more than tokens |
| Claude Sonnet 5 | $2 / $10 | 1M | The workhorse tier: most tasks never need more |
| GPT-5.6 | $2 / $10 | 1.1M | OpenAI's flagship; on listed prices, output-heavy work costs more here than Opus |
| Gemini 3.1 Pro | $2 / $12 | 1M | Grounded search in the loop - our research workhorse for current-state questions |
| MiniMax M3 | $0.3 / $1.2 | 1M | Budget text throughput; weak agentic scores on current comparisons |
| Model | $/Mtok in / out | Context | The one-line read |
|---|---|---|---|
| Kimi K3 | $3 / $15 | 1M | Open weights at frontier coding scores - the strongest open release to date |
| DeepSeek V4 Pro | $1.6 / $3.2 | 1M | The hosted open-weight default for serious agent loops |
| DeepSeek V4 Flash | $0.089 / $0.18 | 1M | The volume tier: bulk pipelines at pennies per million |
| Qwen3.6 27B | $0.3 / $2 | 262K | What we run on our own 4090s - the local-fleet reference point |
| Llama 3.3 70B | $0.1 / $0.32 | 131K | The open-weight baseline every host carries |
| gpt-oss-20B | $0.03 / $0.13 | 131K | OpenAI's open-weight small model - near-free at volume |
Prices live from OpenRouter's public model list · * = curated snapshot where unlisted · benchmarks and reads as reported
The local fleet: extraction, formatting, TypeScript scaffolding, casual vision, bulk drafts. Free after hardware, and every capability claim benchmarked on our own rig.
DeepSeek V4 Pro as the middle tier for high-volume agent loops that outgrow local capability - once it passes the same correctness gauntlet the local models face. Reported numbers don't route real work here; ours will.
Claude for orchestration, architecture, hard debugging - anywhere being wrong costs more than the tokens. Tool-calling reliability is where the frontier lead is least contested, and our whole delegation model depends on it.