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[ARTICLE · art-109295] src=tokenstead.ai ↗ pub= topic=artificial-intelligence verified=true sentiment=· neutral

Ornith-1.5-9B

Ornith AI released Ornith-1.5-9B, a 9-billion-parameter dense language model, on August 18, 2026, claiming it matches or exceeds larger models like Gemma 4-31B and Qwen 3.6-35B on agentic coding tasks. The model, available under an MIT license on HuggingFace, scores 70.6 on SWE-bench Verified and 86.4 on GPQA-Diamond, with a 262K context window and quantized builds for edge devices.

read3 min views3 publishedAug 24, 2026
Ornith-1.5-9B
Image: Tokenstead (auto-discovered)

consumer**~9B Dense** - the edge-deployable Ornith-1.5, released 2026-08-18. Compact enough for phones (a quantized Ornith-1.5-9B-Mobile variant targets iOS/Android) yet matches or exceeds much larger models like Gemma 4-31B and Qwen 3.6-35B on agentic coding. 262K context, MIT-licensed on HuggingFace at ornith-ai/Ornith-1.5-9B

(GGUF and MLX quantizations). #

**Coding (vendor self-reported):** Terminal-Bench 2.1 46.2, SWE-bench Verified 70.6, SWE-bench Pro 47.5, NL2Repo 32.4. -
**Reasoning:** HLE 20.2 (no tools) / 30.5 (with tools), GPQA-Diamond 86.4. -

Agentic: MCP-Atlas 54.2, Toolathlon-Verified 41.2, ClawEval 66.5.

Runs almost anywhere. Quantized builds run on phones, laptops, and small GPUs; the strongest open-weight coding model at this size. Vendor benchmarks are claims pending independent replication.

  • 9.0B
  • 262k
  • mit
  • 🇺🇸 USA
  • Aug 2026

Scores #

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 Q5_K_M Q6_K Q8_0 BF16
4x H100 80GB (320GB) fast 1274.7t/s fast 1109.6t/s fast 974.6t/s fast 752.7t/s fast 400.5t/s
NVIDIA DGX Station 748GB fast 761.0t/s fast 662.5t/s fast 581.9t/s fast 449.4t/s fast 239.1t/s
4x RTX 5090 (128GB) fast 681.8t/s fast 593.6t/s fast 521.3t/s fast 402.6t/s fast 214.2t/s
4x RTX 4090 (96GB) fast 383.5t/s fast 333.9t/s fast 293.3t/s fast 226.5t/s fast 120.5t/s
2x RTX 5090 (64GB) fast 340.9t/s fast 296.8t/s fast 260.7t/s fast 201.3t/s fast 107.1t/s
2x RTX 3090 (48GB) fast 178.1t/s fast 155.1t/s fast 136.2t/s fast 105.2t/s fast 56.0t/s
Single RTX 5090 (32GB) fast 170.5t/s fast 148.4t/s fast 130.3t/s fast 100.7t/s fast 53.6t/s
Mac Studio M4 Ultra 192GB fast 113.3t/s fast 98.6t/s fast 86.6t/s fast 66.9t/s fast 35.6t/s
Mac Studio M4 Ultra 512GB fast 113.3t/s fast 98.6t/s fast 86.6t/s fast 66.9t/s fast 35.6t/s
Single RTX 4090 (24GB) fast 95.9t/s fast 83.5t/s fast 73.3t/s fast 56.6t/s fast 30.1t/s
MacBook Pro M5 Max 128GB fast 63.7t/s fast 55.5t/s fast 48.7t/s fast 37.6t/s fast 20.0t/s
Single GTX 1080 Ti (11GB) fast 46.0t/s fast 40.1t/s fast 35.2t/s tight

no -> cloudFit 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.

Download options #

Or run it in the cloud #

No per-token API provider pricing tracked for Ornith-1.5-9B yet.

For flagship list prices, see the
[calculator](/calculator).

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.

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