Qwen3.6 27B - cheapest: Morph $0.29/M input

Morph offers the cheapest API pricing for Qwen3.6 27B at $0.29 per million input tokens, according to live provider data refreshed about one hour ago via OpenRouter. The 27-billion-parameter model, released in November 2025 under Apache 2.0, scored 1.00 on tool-efficiency benchmarks and is described as the best local agent and tool-use model as of June 2026.

Qwen3.6 27B enthusiastBest local agent and tool-use model as of June 2026. Scored 1.00 on tool-efficiency benchmarks. Best choice for agentic workflows. - 27.0B - 128k - apache 2.0 - Nov 2025 Scores Score per dollar 284 pts per $/M input general score 82 divided by cheapest input price $0.29/M . Higher is better value. See live pricing /models/qwen3-6-27b/pricing . Run it locally Per-quant memory needs and a static "can you run it?" reference - no rig entry required Can you run it? - reference rigs | Rig | Q4 K M | Q8 0 | |---|---|---| | NVIDIA Jetson Orin NX 16GB | | no - cloud cloud-pricing no - cloud cloud-pricing Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast =20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark. How can a 24GB GPU run a 744B model? It does not load the model into VRAM. The quantized weights e.g. ~410GB at Q4 sit in system RAM; the GPU only holds the small shared attention and router tensors and accelerates prompt processing. Because GLM 5.2 is a Mixture-of-Experts model, each token activates only ~40B of its 744B params, so llama.cpp streams just those active experts from system RAM to the GPU each token the -cmoe offload path . That makes decode speed bound by system-RAM bandwidth, not GPU bandwidth - single digits on DDR4, which is why these rigs show 3-8 t/s even though they “fit.” A bigger GPU e.g. 2x 3090 keeps more experts resident on-card and raises tok/s; a smaller GPU still runs it but pays the bandwidth tax. A 744B dense model could not run this way - only MoE’s small-active-params trick makes it possible. Aggressive quants 1-2 bit trade accuracy for size - roughly 17% accuracy loss at 2-bit vs full precision, and real long-context work often needs Q5 or Q6 even when lower quants “fit.” Formula estimates here are conservative; real tuned setups can exceed them one HN user reports ~6 tok/s on a 512GB DDR4 + 2x 3090 rig . Download options Or run it in the cloud Live per-provider pricing, throughput and uptime - refreshed about 1 hour ago via OpenRouter. Click a column to sort. | Provider | Type | Input $/M | Output $/M | Cache $/M | Tok/s | Latency | Uptime | Value | |---|---|---|---|---|---|---|---|---| | DeepInfra | API | 0.32 | 3.20 | - | - | - | 100.00% | best uptime | | Io Net | API | 0.38 | 3.20 | 0.200 | - | - | 99.96% | | | Alibaba | API | 0.45 | 2.70 | - | - | - | 99.77% | | | Venice | API | 0.32 | 3.25 | - | - | - | 99.26% | | | Morph | API | 0.29 | 2.40 | - | - | - | 99.17% | cheapest | | WandB | API | 0.60 | 3.60 | 0.120 | - | - | 98.91% | | | Phala | API | 0.32 | 2.70 | 0.150 | - | - | 97.33% | | | SiliconFlow risky | API | 0.30 | 3.20 | - | - | - | 93.94% | | | Chutes risky | API | 0.30 | 2.00 | 0.150 | - | - | 92.70% | Default order: throughput among 95%+ uptime providers, then latency; subscriptions last. Sort by any column. Subscription rows show $/mo in the Value column - per-token columns are "-". Affiliate links are marked sponsored / nofollow. Confirm current pricing on the provider's site before committing. Detailed API pricing page + JSON endpoint → /models/qwen3-6-27b/pricing See who runs Alibaba in production → /adoption/alibaba Inference cost over time Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.