Benchmarking the Claude Agent SDK on a local LLM: Haiku and Sonnet tier performance

A developer benchmarked the Claude Agent SDK on a local LLM, finding that a 35B-parameter model running on an RTX 3090 Ti can replace Anthropic's Haiku tier in production. The local model achieved parity on the dominant workload (verify-rag-fact) with a 5.8x speedup, and outperformed the Anthropic ceiling on importance scoring. However, the extract-chunk workload scored 66-83% of the ceiling, requiring human spot-checks until the gap is closed.

The Claude Agent SDK exposes three budget tiers haiku , sonnet , opus and reads its routing target from environment variables on every call. That means a single env-var swap can point a tier at any Anthropic-compatible endpoint — including a local llama-server . The question is not whether you can do it. The question is whether the local model is good enough, per tier, to ship. This article is the benchmark we ran to answer that question for Anatoly's document fact-check pipeline. 5 providers × 4 workloads × N=5 trials , Opus LLM-as-judge with an Anthropic-vs-Anthropic ceiling for calibration. Host: one RTX 3090 Ti, 24 GB VRAM. | Tier | What it does in our pipeline | Calls per run | Profile | |---|---|---|---| Haiku | extract atomic facts, verify them against retrieval, score importance | ~1,700 | JSON-only, low-latency, strict schema | Sonnet | rewrite document sections to integrate omissions and correct hallucinations | ~8 | markdown out, ~3 k tokens, citation-tag sensitive | Opus | reserved for final review and high-stakes work | varies | stays on Anthropic regardless | The two questions: Five providers cycled sequentially through four workloads, N=5 trials, 100 calls total, 0 errors: parallel=1 parallel=2 the 1.5 GB freed by 4-bit KV makes the extra slot viable Three quality signals per output, all measured against the Anthropic reference: The Opus judge is the load-bearing one. Crucially, we also rate Anthropic trial 2 against Anthropic trial 1 as the empirical ceiling: the score "indistinguishable" gets for two runs of the same backend on the same input. Any local provider at the ceiling is at parity, empirically , not approximately. Three workloads: extract-chunk ~88 calls/run , verify-rag-fact ~1,300 calls/run , importance-score ~300 calls/run . All JSON-only with strict schemas. | Workload | Anthropic | local-turbo-parallel | Speedup | |---|---|---|---| | extract-chunk | 53.80 s | 5.79 s | ×9.3 | | verify-rag-fact | 10.90 s | 1.87 s | ×5.8 | | importance-score | 10.39 s | 2.42 s | ×4.3 | | Workload | Anthropic ceiling | Local production: 35 B no-think | Reading | |---|---|---|---| | verify-rag-fact | 9/10 | 9/10 | at ceiling 1,300 calls/run, dominant cost | | importance-score | 8/10 | 9/10 | one point above ceiling 35B picks borderline buckets cleaner than Anthropic Haiku | | extract-chunk | 6/10 | 4–5/10 | 66–83 % of ceiling Anthropic is strict on its own output here | Verdict on the Haiku tier : the local 35B-A3B in no-think mode is at parity on the dominant workload verify, ×5.8 faster and one point above ceiling on importance. Extract is one to two points below ceiling; the gap is real but small, and the upper bound matches the ceiling. The local 35B can replace Anthropic Haiku in production , with the caveat that extract benefits from human spot-checks until we close that gap. A surprise from a follow-up mini-bench: a 35B-A3B MoE in no-think mode outperforms a dedicated dense 4B at essentially the same latency, because MoE only activates ~3 B parameters per token. | Config importance workload | Wall mean | Stdev | Opus rating | |---|---|---|---| | 4 B no-think | 2.13 s | 0.02 s | 8/10 | 35 B no-think | 2.42 s | 0.15 s | 9/10 | We initially shipped two separate models 4B for haiku, 35B for sonnet . After the mini-bench we collapsed to one GGUF in two containers, distinguished only by a thinking flag. One workload: correct-section-rewrite ~8 calls/run . Markdown output, ~1.5 to 3 k tokens, requires filename citation tags on every inserted sentence. | Provider | Wall mean | Note | |---|---|---| | Anthropic Sonnet | 26.01 s | reference | | Anthropic Opus | 12.88 s | the actual Anthropic option for this workload | | local Qwen 35B no-think | 9.61 s | fastest | | local Qwen 35B thinking ON | 23.50 s | thinking helps quality, hurts latency | | Provider | Opus rating | Failure mode | |---|---|---| | Anthropic Opus | 9/10 | reference | | local Qwen 35B thinking ON | 6/10 | misses filename citation tags | | local Qwen 35B no-think | 5/10 | misses citation tags, slight formatting drift | Verdict on the Sonnet tier : local does not replace Anthropic on this workload . The local 35B applies every requested fix Opus judge: "all four hallucinations softened and all three omissions integrated" , but consistently omits the filename citation tags Opus produces by default. No combination of thinking flags, prompt tweaks, or larger context closed the gap. The shortfall is 3 to 4 Opus-judge points on a 0-10 scale. So we ship hybrid: local for the Haiku tier middle, Anthropic Opus for the 8 Sonnet-tier rewrites . Those 8 calls cost ~$4 per run, but they buy the full ceiling on the content-touching phase, which matters more for the user than the cost. | Full Anthropic | Production hybrid local middle + Opus rewrite | Delta | | |---|---|---|---| | Anthropic API calls per run | 1,696 | 8 | −99.5% | | Wall-clock end-to-end | ~4 h | ~59 min | −75% | | Cost per run | ~$5 | ~$4 | −20% | | Verify quality | 9/10 | 9/10 parity | 0 | | Rewrite quality | 9/10 | 9/10 still Opus | 0 | The cost saving is modest because the 8 Anthropic calls we keep are on Opus expensive per call but mandatory for quality . The volume reduction is the headline: 99.5% fewer calls means our Anthropic quota is no longer the bottleneck for the rest of the product. SDK env vars per call │ ├─ haiku ──► local llama-server, Qwen3.6-35B-A3B GGUF, thinking OFF ├─ sonnet ──► defined but not loaded in production; falls back to Opus └─ opus ──► Anthropic native API for correct-phase rewrite Same GGUF in both LLM containers, different thinking flags. ~10 s restart to switch tiers. TurboQuant 4-bit KV cache + --parallel 2 for throughput. For Anatoly, the practical impact: The benchmark above is contingent on four SDK integration fixes. They are not exotic, but none are obvious from the docs: --alias on llama-server /v1/models validation accepts a stable name local-haiku , local-sonnet instead of the GGUF filename. parallel=1, ctx per slot=32768 --ctx-size by --parallel for per-slot context; defaults give only 4 k per slot . AskUserQuestion , Skill , CronCreate , ScheduleWakeup , and ~20 others by default. Sonnet ignores them politely; Qwen happily calls AskUserQuestion to "think out loud" and burns the max turns budget. /no think directive is honoured 8% of the time on Qwen3.5+. Fix at llama-server startup: llama-server \ --jinja \ --reasoning off \ --chat-template-kwargs '{"enable thinking": false}' \ ... The last one is the biggest single perf lever: ×12 speedup on the importance workload 21.7 s → 1.83 s wall mean because the model stops emitting 358 tokens of reasoning before the 9-token JSON answer. Full write-up with the TurboQuant Dockerfile, the build gotchas libcuda.so.1 , libgomp.so.1 , the fork comparison, the complete per-workload Opus-judge tables, and the N=5 variance discussion: https://anatoly.cloud/research/local-llm-claude-agent-sdk-turboquant https://anatoly.cloud/research/local-llm-claude-agent-sdk-turboquant