A card is one canonical model identity: aliases, whether Continuum hosts it, and any coding-agent score we could fetch with the harness still visible. Vendor copy is labeled vendor. Independent boards are labeled independent. We omit a chip rather than invent a percent.
Each card is one identity, not a SKU dump. This cluster publishes 223 cards across 25 labs (at least two per vendor). OpenRouter This Week tokens and official Hugging Face downloads are the two sort keys on this index. Preview dates that did not earn a card stay on the vendor hub as lineage. Fable 5 is a section in Claude Code models; it also has a full card here because buyers search the name.
Vendor means the lab that ships the weights or the API. Independent means a board we opened: DeepSWE official mini-swe-agent (v1.1 Best, 113 tasks, board updated 13 Aug 2026), Artificial Analysis Intelligence Index integers, and vals.ai hero-index percents where we opened a card. Scale SWE-bench Pro and Terminal-Bench 2.1 stay omitted cluster-wide.
Open-weight cards link the official Hugging Face repo (Files and Use this model). Closed-weight cards have no fake download. Continuum hosted ids come from the live public allowlist on 19 Aug 2026, not from a guess.
Source of record: OpenRouter This Week, usage through 18 Aug 2026, fetched 19 Aug 2026. Tokens are prompt plus completion. The percent on a leaderboard row is week-over-week change, not share of the week. Market share on this page is text request share by author.
OpenRouter This Week tokens are prompt plus completion. The percent next to a row is week-over-week change, not share of the week. Market share on this page is text request share by author, a different number.
| # | Model | Author | Tokens | WoW |
|---|---|---|---|---|
| 1 | DeepSeek V4 Flash 0731 | deepseek | 11.3T | +12% |
| 2 | Hy3 | tencent | 9.83T | +4% |
| 3 | GPT-5.6 Luna | openai | 5.8T | +19% |
| 4 | MiMo-V2.5 | xiaomi | 5.46T | +9% |
| 5 | DeepSeek V4 Flash 0423 | deepseek | 4.78T | −13% |
| 6 | GLM 5.2 | z-ai | 4.34T | +18% |
| 7 | Gemini 3.6 Flash | 2.75T | +12% | |
| 8 | Nemotron 3 Ultra (free) | nvidia | 2.69T | +20% |
| 9 | Claude Opus 5 | anthropic | 2.68T | +89% |
| 10 | DeepSeek V4 Pro 0423 | deepseek | 2.57T | +1% |
| 11 | MiniMax M3 | minimax | 1.67T | +2% |
| 12 | Laguna S 2.1 (free) | poolside | 1.67T | −5% |
| 13 | Kimi K3 | moonshotai | 1.32T | −9% |
| 14 | Claude Sonnet 5 | anthropic | 1.08T | +5% |
| 15 | DeepSeek V4 Pro 0813 | deepseek | 946B | new |
| 16 | GPT-5.6 Terra | openai | 943B | +12% |
| 17 | Step 3.7 Flash | stepfun | 857B | −30% |
| 18 | Nemotron 3.5 Lightning (free) | nvidia | 836B | +999% |
| 19 | Gemini 3.7 Flash | 830B | new | |
| 20 | Gemini 3 Flash Preview | 821B | −8% |
Rails on this board are relative: token volume has no natural ceiling, so each fill is that row's tokens against the largest row here. Read them against each other, not as a percentage.
Second sort key: official Hugging Face downloads on the text-generation and image-text-to-text repos we carded. Hub API 2026-08-19. Popularity, not quality. Embedding, MiniLM, BERT, BGE, CLIP, and TTS boards are omitted.
| # | Model | Author | Downloads | Hub |
|---|---|---|---|---|
| 1 | Qwen3-0.6B | qwen | 28,202,544 | Qwen/Qwen3-0.6B |
| 2 | Qwen3 8B | qwen | 15,796,910 | Qwen/Qwen3-8B |
| 3 | Qwen3.5-9B | qwen | 13,604,767 | Qwen/Qwen3.5-9B |
| 4 | Qwen2.5-7B-Instruct | qwen | 12,229,058 | Qwen/Qwen2.5-7B-Instruct |
| 5 | Gemma 4 26B A4B | 9,533,369 | google/gemma-4-26B-A4B-it | |
| 6 | Gemma 4 31B (free) | 9,311,525 | google/gemma-4-31B-it | |
| 7 | Llama 3.2 1B Instruct | meta | 8,694,182 | meta-llama/Llama-3.2-1B-Instruct |
| 8 | gpt-oss-20b | openai | 7,682,588 | openai/gpt-oss-20b |
| 9 | Llama 3.1 8B Instruct | meta | 7,199,331 | meta-llama/Meta-Llama-3.1-8B-Instruct |
| 10 | R1 | deepseek | 6,911,569 | deepseek-ai/DeepSeek-R1 |
| 11 | Qwen3.6 27B | qwen | 6,745,154 | Qwen/Qwen3.6-27B |
| 12 | Qwen3.6 35B A3B | qwen | 5,663,515 | Qwen/Qwen3.6-35B-A3B |
| 13 | Qwen3 VL 8B Instruct | qwen | 5,280,026 | Qwen/Qwen3-VL-8B-Instruct |
| 14 | Gemma 3 1B IT | 4,983,962 | google/gemma-3-1b-it | |
| 15 | gpt-oss-120b | openai | 4,657,776 | openai/gpt-oss-120b |
| 16 | Qwen3.5-27B | qwen | 2,837,939 | Qwen/Qwen3.5-27B |
| 17 | GLM 5.2 | z-ai | 2,748,563 | zai-org/GLM-5.2 |
| 18 | Qwen3.5-35B-A3B | qwen | 2,427,827 | Qwen/Qwen3.5-35B-A3B |
| 19 | DeepSeek V4 Flash | deepseek | 2,330,940 | deepseek-ai/DeepSeek-V4-Flash-0731 |
| 20 | Kimi K3 | moonshotai | 2,289,863 | moonshotai/Kimi-K3 |
/models sorted Top Weekly extends past the 20-row LLM Leaderboard and uses slightly different totals because it counts all endpoints. Sol is #18 there at 973B while Lightning is #18 on the leaderboard. We cite both lists and do not average them.
Hub order is locked: unique authors, token-first from the This Week board, then request share, then named labs. Moonshot and StepFun stay because they are on the week board. xAI, Meta, and Qwen stay as named plus request-share labs. Week-token cells name the SKU we actually counted. We do not invent a figure for a lab the boards did not cite.
Hero tiles are DeepSWE (official v1.1 Best, mini-swe-agent, 113 tasks, board updated 13 Aug 2026; effort labeled), Artificial Analysis Intelligence Index (published integer), and the vals.ai hero index percent where we opened a card. Missing benches are omitted, not invented. Every chip carries source URL and as-of.
Scale SWE-bench Pro is cited only from the public shared-harness board. We could not extract a published per-model row from labs.scale.com on 19 Aug 2026 (client-rendered). Terminal-Bench 2.1 is pinned to Artificial Analysis; their model pages did not expose a standalone Terminal-Bench 2.1 score in server HTML. Both stay omitted cluster-wide.
LiveCodeBench is a contest footnote if we ever have a sourced row. SWE-bench Verified is historical and never the hero. Artificial Analysis cost and speed, when fetched, sit under Economics as a footnote, not a substitute for the Intelligence Index integer.
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