Claude Haiku 5.5 vs Qwen3.6 27B

Claude Haiku 5.5: where it fits

Anthropic's October 7, 2026 small-model refresh — a 1M-token context window (up from 200K) at roughly 75% lower average cost than Haiku 4.5, priced at $0.10/$0.50 per million tokens. Released October 7, 2026 by Anthropic, it is built for 5x the context window of Haiku 4.5 (1M vs 200K tokens) and double the max output (128K vs 64K), first Haiku-class model with an adjustable 'effort' setting to trade off cost, latency, and intelligence, anthropic's cheapest small model yet — runs about 75% cheaper on average than Haiku 4.5, and over-refuses benign requests only 0.17% of the time on the API, down from Haiku 4.5's 0.44%.

Its trade-offs are real: pricing jumps roughly 5x beyond 100K-token prompts ($0.50/$2.50 per million vs the headline $0.10/$0.50), extended thinking can no longer be fully disabled — only dialed between low, medium, and high effort, and as the smallest tier, trails both Sonnet 5.5 and Opus 5.5 on hard reasoning and open-ended work. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.

Qwen3.6 27B: where it fits

A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Released April 22, 2026 by Alibaba, it is built for the best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size, dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised, far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0), and dense models fine-tune far more predictably than mixture-of-experts models do.

Its trade-offs: every parameter fires on every token, so it is slower and costlier per token than the sparse 35B, hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter, and its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

The defining split here is open vs. closed. Qwen3.6 27B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Claude Haiku 5.5 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

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