Posts in the AI Model Tracker series
Gemini 3.7 Flash (API ID: gemini-3.7-flash): 1M input / 64k output context, input $0.75, output $3.75 per 1M tokens (promotional pricing through 2026-12-31, reverting to $1.50 / $7.50 — same as predecessor 3.6 Flash); DeepSWE v1.1 65.3% (prev 48.6%), AutomationBench 30.4% (prev 17.0%), FrontierCode 1.1 43.6%; beats Claude Sonnet 5 and GPT-5.6 Terra on multiple agentic/enterprise automation benchmarks
Muse Glimmer (HF: meta-models/Muse-Glimmer-30B): 29.6B params, 131K+ context, Apache 2.0 fully open-source, zero token cost for local deployment; MCP Atlas 75.5 (vs Gemma4-31B 54.2, Qwen3.6-27B 62.5), SWE-Bench Pro 51.2 leads same tier, but trails Qwen3.6-27B on OSWorld-Verified and TerminalBench 2.1; 4-bit quantized fits under 20GB, DFlash speculative decoding delivers 3.1x speedup on RTX 5090
Grok 4.6: 500K-token context window, $2 input / $6 output per 1M tokens (same as 4.5), AA Intelligence Index 61 (tied with GPT-5.6 Sol Max), GDPVal-AA v2 1753 Elo (highest overall), but DeepSWE and Terminal-Bench still trail GPT-5.6 Sol and Claude Fable 5
Muse Spark 1.2: 1M context window, input $1.25 / output $4.25 per 1M tokens (same as 1.1), AA Intelligence Index 57, GDPval-AA v2 Elo jumps 260 points to 1631 (5th overall), paired with Meta's first code agent Muse Code for long-running multi-agent collaboration
GLM-5.3: same GLM-5.2 base model, pure post-training gains, 1M context / 128K max output, pricing unchanged at $1.4 input / $4.4 output (per 1M tokens), Terminal-Bench 3.0 jumps from 4.6% to 28.3% (open-source SOTA), CyberGym vulnerability discovery 84.5% surpasses every listed closed-source frontier model. Zhipu delayed weight release until safety evaluation completes (~8/28)
Wan3.0: single-shot length doubles from Wan2.7's 15s to 30s, up to 1080P, supports doc/xls/ppt/pdf/md files and web pages as generation inputs, priced at 480P $0.05 / 720P $0.10 / 1080P $0.20 per second — roughly 50% cheaper than Google Veo 3.1 Standard, but now closed-source API-only, and not yet independently tested by third parties
Qwen3.8-Flash-Next: open-weight preview of the Qwen4 architecture, 125B total parameters with only 6B active (plus a 51B N-gram embedding), 262K native context extensible to 1M, Qwen Community License 1.0 (not Apache 2.0). Official benchmarks show it beating both its own 27B dense model and the 397B Qwen3.7-Plus on agentic coding (DeepSWE 1.1: 58.7) and scoring highest on CoWorkBench long-horizon office tasks (73.9) — but no official API pricing or independent third-party testing exists yet
GLM-5.3-Flash: 320B total / 18B active parameters (MoE), 1M context / 131K max output, natively accepts text + image + video input, MIT-licensed weights on HuggingFace; standard pricing $0.15 input / $0.50 output per 1M tokens (50% launch discount to $0.075/$0.25 through Sept 9), roughly 90% cheaper than sibling model GLM-5.3; Terminal-Bench 2.1 hits 84.3 (just behind Opus 4.8's 85.0), DeepSWE 1.1 jumps from GLM-5.2's 46.2 to 63.4; under its 'Ox Alpha' alias it briefly took the #1 weekly token share spot on OpenRouter
Tencent Hy4 preview: 770B total / 49B active parameters (MoE, 78 layers), 1,048,576-token context window; API pricing $0.834 input / $2.501 output per 1M tokens (cache hit $0.042); Apache 2.0 open weights on HuggingFace; a 163-engineer blind eval scores it 2.99/4.00, just ahead of GLM-5.3 (2.92) and Kimi K3 (2.94); third-party aggregator BenchLM scores it 79.2/100, ranked #7 of 228 models; Tencent discloses for the first time that the model helped optimize its own training pipeline and inference system, lifting throughput 31.8%
Breeze TTS 2: open weights (Apache 2.0 code, research/non-commercial model license), #1 open-weights model on Artificial Analysis Provider Voices (1,215 Elo, +90 over Fish Audio S2 Pro), #1 on both Voice Design (Role Fit 78.02) and Voice Direction (4.25) benchmarks; TTFA p50 133.6ms / p95 163.3ms, RTF 0.32 on H100; hosted API priced at $34 per 1M characters (over 2x Fish Audio S2 Pro); supports 50 languages, commercial use requires a separate license from RESONIA, INC.
DeepSeek-V4-Flash-Vision-Exp: 284B total / 13B active-parameter MoE, 1M context, 384K max output; priced identically to plain V4-Flash (Input $0.44 / Output $1.32 at peak, half that off-peak); wins 6 of 7 text-agent benchmarks against its own predecessor (DeepSWE hits 59.3, edging past Opus-4.8's 58.0); multimodal-agent scores close in on Opus-4.8 (trails by 2.9 on ApexBench, actually leads on ZeroBench); each image is capped at 384 tokens / roughly 800×800 resolution, trading fine detail for near-zero cost
Gemini Omni 1.1 Flash (gemini-omni-1.1-flash): GA since 2026-08-27, replacing the preview that launched 6/30; closed-source, priced per output second: $0.03 at 360p, $0.10 at 720p (default), $0.15 at 1080p, $0.30 at 4K (1080p/4K are upscaled); ranks #1 on the Artificial Analysis Text-to-Video Arena without audio (1322 Elo) and #2 with audio (1237, trailing Wan3.0's 1241); adds scene extension (10s of context, chainable up to 40s total) and first/last-frame interpolation for camera control
Muse Voice Transcribe (muse-voice-transcribe-1.0): Meta Superintelligence Labs' first real-time audio perception model, launched 2026-09-01; closed-source, API-only, $0.18/hour of audio ($3.00 per 1,000 minutes); 3.1% final-transcript WER on streaming (#1 on Artificial Analysis AA-WER Streaming, ahead of Cartesia Ink-2's 3.4%), 0.16s delay from end-of-speech to final transcript; one model does ASR, 20+ speaker diarization, and endpointing together, replacing what used to require three separate systems
Gemini 3.8 Flash (gemini-3.8-flash): launched 2026-09-02, 1,048,576 input tokens / 64,000 output tokens, $0.75 input / $3.75 output per 1M tokens (introductory rate through 2026-12-31, then $1.50/$7.50); Terminal-Bench 2.1 90.8% (up from 81.6%), DeepSWE v1.1 73.7% (up from 65.3%); ships alongside Gemini 3.8 Flash Cyber, a security variant gated behind the Fairwind Program, scoring 47.2% pass@1 on CWE-Bench and exceeding 70% on a real-world 20-language vulnerability-discovery benchmark
K2 Horizon 375B-A23B (IFM/K2-Horizon-375B-A23B): released 2026-09-03 by the Institute of Foundation Models (under MBZUAI, Abu Dhabi), 375B total / 23B active parameters (MoE), 512K (524,288 tokens) native context, fully open under Apache-2.0 (weights, code, training data recipes, and intermediate checkpoints all public), no official API pricing (open weights, self-hosted); Terminal-Bench 2.1 70.2%, SWE Bench Pro 42.6%, SWE-Atlas-QnA 48.4% (highest of any model tested, open or closed); shipped alongside five sibling sizes — 36B-A4B (new MoVA sparse attention), 32B, 7B, 3.7B, 0.9B
GPT-6 Astra (API ID: gpt-6-astra): released by OpenAI on 2026-09-03, 1,050,000-token context window, 128,000 max output tokens, input $10.00 / output $50.00 per 1M tokens (cached input $1.00), closed-source; 99.9% on ARC-AGI-3 under OpenAI's own harness (62.7% on the standardized harness), 97.6% on FrontierMath Tier 4, 100% on ExploitBench; the first model rated 'Critical' cybersecurity capability under OpenAI's Preparedness Framework; yet scores only 61 on the neutral Artificial Analysis Intelligence Index — tied with predecessor GPT-5.6 Sol and behind Claude Fable 5.1's 66
Claude Fable 5.1 (API ID: claude-fable-5-1): released by Anthropic on 2026-09-01, 1,000,000-token context window, 128,000 max output tokens, input $10.00 / output $50.00 per 1M tokens (cache reads cut to $0.25, down 75%), closed-source; 52.6% on Terminal-Bench-Science 0.1 self-reported (up from 24.7%), 66 on the neutral Artificial Analysis Intelligence Index (up from 62, ahead of GPT-6 Astra's 61); launched alongside the restricted Claude Mythos 5.1, available only to vetted cybersecurity and life-sciences organizations
MiniCPM5-2B (openbmb/MiniCPM5-2B): quietly published to Hugging Face by OpenBMB on 2026-09-06 with no official blog announcement; 2.6B dense parameters, 131,072-token context window, fully open under Apache-2.0. Scores 15 on the neutral, third-party Artificial Analysis Intelligence Index v4.2 — the highest of any open-weights model under 4B parameters (next best, Granite 4.2 3B, scores 11). Leads the set on GDPval-AA v2 (real-world work tasks) with an Elo of 831. Already deployed day-zero across 9 AI chips via FlagOS, including Huawei Ascend and NVIDIA.
DeepSeek-V4.1-Flash (`deepseek-flash`) is a 552B MoE with 8B prefill and 16B decode active parameters, a 1M-token context window, and 384K maximum output. It is MIT-licensed, priced at $0.30 input and $1.20 output per million peak-hour tokens, and brings CED, CSA2, and FP4 KV-cache compression to agent workloads.
Nex-N2.5-mini (`nex-agi/Nex-N2.5-mini`) is a 35B MoE with about 3B active parameters, a 262,144-token context window, Apache-2.0 licensing, and a free OpenRouter tier. Nex reports large gains over N2-mini on DeepSWE and Terminal-Bench, with visual-feedback self-correction as its central design goal.
DeepSeek-V4.1-Flash (API model: deepseek-flash): released 2026-09-10, 552B-parameter MoE with a Causal Encoder-Decoder architecture (only 8B active parameters for input, 16B for output), 1M-token context, MIT-licensed. Peak pricing: input $0.30 / output $1.20 per 1M tokens (off-peak halved) — cheaper than the predecessor V4-Flash. Terminal-Bench 4.0 jumps from 7.0 to 31.2, DeepSWE v1.1 (74.2) ties Claude Opus 5 (74.0). KV cache compressed to 890 bytes/token (1/4 of the predecessor, 437x less than DeepSeek-V1). DeepSeek announced that starting Sept 14, all traffic to its own V4-Pro flagship will be routed to this Flash model and billed at Flash rates.
Agnes 3.0 Flash (API model: agnes-3.0-flash, vendor: Agnes AI / Singapore's Sapiens AI): launched 2026-09-09, 512K context, input $0.05 / output $0.15 / cached input $0.005 per 1M tokens (currently free during a promo period). Scores 36 on the Artificial Analysis Intelligence Index v4.3, tying DeepSeek V4 Pro, while generating output at 235-252.7 tokens/s — over 3x V4 Pro's 72 t/s. Closed-source, cannot be self-hosted. A separate open-weight Preview checkpoint sharing the same name (33B, 262K context, Apache 2.0) has different architecture and scores — easy to confuse with the production model.
Edge0-35B-A3B-preview (Edge0/Edge0-35B-A3B-preview): open-sourced by Edge0-AI on 2026-09-08. 4-bit quantized, 256 experts with 4 active per token (base model: Qwen3.5-MoE 35B-A3B). Three mechanisms — SSD expert offload, prerouter predictive routing, and Recover-LoRA distillation — let it run on a Mac mini M4 Pro (24GB) with just 2.9 GiB peak memory and 15 tok/s decode, averaging only a 3.9-point loss across 5 benchmarks versus the fp16 base. Fully open under Apache-2.0; also ships an 8B-A1B tier (built on Ling 3.0 Tiny). Agentic capability is currently weak — officially positioned as a preview release.
Xing4.0-29B-A4B (China Telecom Artificial Intelligence Technology / XingChen-AGI, successor to TeleChat): 29B total parameters with only 4B active, 256K native context (extensible to 512K), Apache-2.0 open source. Scores 75.0 on SWE-bench Verified (vs. 76.0 for the larger Qwen3.6-35B-A3B and 53.0 for Gemma4-26B-A4B) and 57.5 on Terminal-Bench 2.1, the highest of the three. It's the first model at this scale trained entirely from scratch on Ascend 910C + MindSpore, with training throughput improved roughly 96% over an unoptimized baseline.
Ling-3.0-flash-Fin (Ant Group): Released at the 2026 Inclusion·Conference on the Bund (2026-09-09), 124B total parameters with 5.1B active, 256K context (scalable to 1M), MIT open-source; built on Ling-3.0-flash's MoE architecture and long-context capabilities with continued financial-domain pretraining, tested across seven financial benchmarks including FinFIRST, FinSearchComp Verified, FinCRAFT, Finance Agent, APEX-Agents, SpreadsheetBench, and τ³-Banking; AA Intelligence Index v4.1.1 improved from 38 to 41; weights available on Hugging Face and ModelScope, OpenRouter offers a one-month free API
Jev (TypeSafe AI): launched in early access on 2026-09-15, non-autoregressive architecture that takes 'state + typed questions' instead of natural-language prompts and returns probability distributions with confidence scores instead of text; input priced at $0.042/1M tokens, output free; TypeSafe's own workflow benchmark shows 193.6x speed and 444.6x cost advantage over LLMs, though this is self-reported and unreplicated; the company also raised a $40M seed round led by DCVC at a $200M valuation; for agent builders, Jev is best framed as a cheap routing/classification/guardrail layer rather than a generative-LLM replacement
Qwen-Image-2.1 (Alibaba Qwen): open-sourced 2026-09-20, a 7B visual generation module (32-layer single-stream DiT) plus a Qwen3-VL 8B encoder, keeping the same 7B class as predecessor 2.0 but adding native transparent-image generation (64-channel RGBA VAE) and multi-image editing with up to 10 reference images; Qwen's own Qwen-Image-Bench score is 60.28, ahead of Nano Banana 2.0 (59.82) and GPT Image 1.5 (59.65) ⚠️ self-reported, not independently reproduced; the SGLang team independently verified a single-sample RGBA PSNR of 60.69 dB; licensed under the Qwen Research License Agreement, non-commercial only, with no official hosted API pricing; for agent development it can remove a separate background-removal/compositing step, making it a fit for e-commerce asset generation and multi-scene storyboard workflows
Step 5 Preview (StepFun): API launched 2026-09-20, open weights due 2026-10-15; a 600B-total, 27B-active sparse MoE with a 92-layer narrow-deep Transformer, 1M-token context, and text/image/video input; API pricing is $1.00 input (cache hit $0.05) / $2.70 output per 1M tokens; scores 44 on the independent Artificial Analysis Intelligence Index, on par with Kimi K3 and Grok 4.6 but behind Claude Opus 5 (51) and the tied GPT-6 Astra / Claude Fable 5.1 (53); its cost per task runs about 42% below Gemini 3.8 Flash at a comparable score; for agent development it's a mid-tier option offering near-frontier intelligence at a fraction of the cost, suited to long-horizon research agents and financial analysis
Grok 4.7 shipped 2026-09-21 as `grok-4.7`: a new, larger base model with longer RL training; 500K-token context window; API pricing unchanged at $2.00 input / $6.00 output per 1M tokens (rising to $4.00/$12.00 above 200K tokens). xAI's own Terminal-Bench 4.0 score jumps from 20.3% to 38.0%, but Artificial Analysis's independent re-test puts it at only 26% — well behind GPT-6 Astra (60%) and Claude Fable 5.1 (55%). It rolled out to all GitHub Copilot plans the same day. For agent builders, it's a 3-8x cheaper option for agentic coding, but complex autonomous terminal work still warrants independent benchmarking before you rely on it.
Claude Opus 5.5 shipped 2026-09-22, model ID `claude-opus-5-5`; 1,000,000-token context window (128K output cap); API pricing is $4.00 input / $20.00 output per 1M tokens (down from Opus 5's $5/$25), with cache reads cut 60% to $0.20; Terminal-Bench 4.0 jumps from Opus 5's 52.3% to 66.4%, and GDPval-AA v2.1 leads at 1846 Elo versus Fable 5.1's 1735 and GPT-6 Astra's 1542; Anthropic's own numbers show it still trailing GPT-6 Astra on AutomationBench and Terminal-Bench-Science; because its biology and cybersecurity capabilities now match Claude Mythos 5.1, it ships with Fable-5.1-level safeguards, and on the API side thinking mode can no longer be turned off while forced tool use now returns an error instead of failing silently
GPT-6 Sol (`gpt-6-sol`) / GPT-6 Luna (`gpt-6-luna`) shipped 2026-09-22; 1,050,000-token context window (922,000-token max input, 128,000-token output cap — same as flagship Astra); Sol prices at $2.00 input / $10.00 output, Luna at $0.10 input / $0.50 output per 1M tokens (a further 50% cut versus GPT-5.6 promotional pricing); Sol hits 33.2% on AutomationBench at xhigh effort, beating Claude Opus 5's 26.9% at roughly 1/11 the cost; DeepSWE v1.1 puts Sol at 68.8% and Luna at 66.6%, closing in on Claude Fable 5.1's 69.9%; in OpenAI's internal simulated deployment testing, severity-3+ misalignment flags dropped from 66 to 42 versus the prior generation
MiMo-V2.6-Pro shipped open-weight on 2026-09-21, model ID (OpenRouter) `xiaomi/mimo-v2.6-pro`; a 1.02T-total / 42B-activated MoE with a 1,048,576-token context window natively handling text, image, video, and audio; API pricing is $0.435 input / $0.87 output per 1M tokens (cached input $0.0036), with the sibling Flash variant at $0.14/$0.28; MIT-licensed; it edges out Claude Opus 5 on AutomationBench v1.0.6 (53.1 vs 50.3) and Terminal Bench 2.1 (89.9 vs 89.1); it also leads the open-weight field on the Artificial Analysis Intelligence Index at 46.32; but it clearly trails closed flagships on Terminal Bench 4.0 (34.9 vs Opus 5's 49.0) and the security-focused ExploitBench (47.9 vs GPT-5.6 Sol's 78.5)
AliceAI-Foundation-80B-A3B-Base: released by Yandex on 2026-09-21, trained fully from scratch (not a Qwen/Llama fine-tune); 80B total / 3B active MoE parameters, 262,144-token context window; Apache-2.0, no official hosted API pricing yet; scores 91.1 on MATH-500, beating similarly-sized Qwen3.5-35B-A3B-Base and the much larger DeepSeek-V4-Flash-Base (284B-A13B); leads every comparison on the Russian-language WikiWebFacts benchmark at 86.5; it's a raw base model with no SFT/RL alignment, so it can't be used as an agent out of the box
Audio8 ASR Infinite: Edge0 open-sourced it on HuggingFace on 2026-09-21. 4B parameters (Voxtral Realtime audio tower + Qwen2.5-3B-Instruct decoder), 30-second rolling KV cache with exact RoPE re-basing for unlimited-length, drift-free 24/7 streaming, plus semantic VAD heads that distinguish thinking pauses from real end-of-turn. Chinese CER (aishell1 1.75%, aishell4 2.89%) crushes Voxtral-Mini-4B-Realtime (16.80%/16.46%) and Nemotron-3.5-ASR-Streaming-0.6B, but English LibriSpeech WER actually loses to Voxtral. The card claims Apache-2.0, but a GitHub issue points out the decoder inherits Qwen2.5-3B-Instruct's non-commercial Qwen Research License — a licensing gap that needs resolving before commercial deployment
GPT-6.1 Sol (`gpt-6.1-sol`): launched at DevDay on 2026-09-29, 1,050,000-token context window (max input 922,000, max output 128,000, same as GPT-6 Sol); standard pricing unchanged at $2.00 input / $10.00 output, but cached input is halved again from GPT-6 Sol's $0.20 to $0.10 (95% below standard input); matches GPT-6 Astra on DeepSWE v1.1 at roughly 1/5 of Astra's cost, beating GPT-6 Sol's best score by 6.4 points; beats Claude Opus 5.5 on AutomationBench at medium effort by 2.2 points at about 1/3 the cost; in the same week OpenAI shelved a flagship upgrade, GPT-6.1 Astra, over safety concerns and did not release it
Naive-N0.5-Flash (HuggingFace: NaiveAI/Naive-N0.5-Flash): open-sourced 2026-09-27 under MIT, a 309B-total/15.5B-active MoE model built on Xiaomi's open MiMo-V2.5 base model with 3.25T further training tokens, replacing every full-attention layer with a hybrid of 39 Sliding-Window Attention layers and 9 DeepSeek Sparse Attention layers; its own NaiveRT inference runtime hits 50 tokens/s standard and up to 2,000 tokens/s in Ultrafast mode; NaiveAI's self-reported SWE-bench Pro score is 73.6 (behind Claude Opus 5.5's 89.9), and it trails same-tier open model DeepSeek V4.1 Flash on DeepSWE, Terminal-Bench, and ProgramBench; announced API pricing is $0.10 input / $0.40 output / $0.01 cache read per 1M tokens, but the API is not yet live; founder Dai Jifeng has an unresolved IP dispute with former employer MiroMind
IQuest-Q1 (`IQuestLab/IQuest-Q1`): open-sourced 2026-09-28, 320B total / 15B active parameters (MoE, 256 experts with 8 active), 524,288-token context window; open weights, self-hosted, no official API pricing; 84.5% on CyberGym (real-world CVE remediation, second only to DeepSeek-V4.1-Flash's 88.1%), 83.2% on Terminal-Bench 2.1, 64.6% on DeepSWE v1.1, 63.0% on NL2Repo; drops into Claude Code or Codex CLI with just an environment-variable swap; the releasing lab, IQuest, had no prior public model track record and shipped weights, inference code, and training methodology together on its first release
Clef (27B, post-trained from Qwen3.8-27B) and Clef-flash (9B, post-trained from Qwen3.5-9B): 64K context window, image/video input support, Clef priced at $0.24/1M input tokens (Clef-flash $0.09, no separate output pricing); 98.5% on BFCL case-exact, 94.2% macro-F1 on BANKING77, 97.4% macro-F1 on CLINC150+OOS — all ahead of Typesafe's Jev. Architecturally it replaces token-by-token generation with a prefill-only scoring pass. It's the third 'decision model' to ship in a single week, after Typesafe Jev, OpenAI's Decisions API, and Amazon's Strands Decider 2B
Kolibri 1 (`Aleph-Alpha/Kolibri-1`, FP8; `Aleph-Alpha/Kolibri-1-BF16` is the full-precision release): open-sourced 2026-10-03, 78.1B total / 3.46B active parameters (MoE, 384 experts with 6 routed plus 1 shared), context window validated up to 1,048,576 tokens (recommended ≤262,144); fully open weights under Apache 2.0, no official API pricing, self-hosted by design; 96.9% on AIME 2025 (vs. 81.9% for the unreleased predecessor Kolibri Origin), 84.3% on GPQA Diamond (EN), 38.1% on τ³-bench Banking (far ahead of the best competitor's 15.5%); trained with the Merlin-Arthur protocol to abstain rather than hallucinate when context doesn't support an answer; trained in Germany, bilingual by design in German and English, aimed at public-sector, industrial, and aerospace deployments under the EU AI Act and GDPR
Gemini 4 Argon: announced 2026-09-30. Google states only the output limit — 1M tokens (up from 64K) — and has not published an input context window (third-party reports of 2M tokens can't be traced to a Google source); introductory pricing is $2.00 input / $10.00 output per 1M tokens, doubling to $4.00 / $20.00 once the promo ends; it sets a new SOTA on DeepSWE v1.1 at 77.9% (vs. 74.2% for Opus 5.5 and 74.1% for GPT-6 Astra), but only scores 55% on FrontierSWE v2, trailing GPT-6 Astra's 65.5%; access is currently limited to trusted security partners through the Fairwind Program — developers, enterprises, and consumers don't have it yet
Cohere Embed 5: announced 2026-09-30. Pro and Fast tiers, 128K context, text + image + mixed-PDF input, 100+ languages; priced at $0.12 (Pro) / $0.08 (Fast) per 1M text tokens, with image tokens at $0.40 for both; on ViDoRe V3 (visually-rich enterprise document retrieval) Pro scores 85.8, up 8.8 points from Embed 4's 77.0, ahead of Voyage 4 Large (83.7) and Gemini Embedding 2 (83.2); Pro and Fast share one embedding space, so you can index with Pro and query with Fast — cross-model combinations only lose 1.6–2.7% on average and don't require rebuilding the index; all figures are Cohere's own benchmarks, not yet independently reproduced
Mistral Large 4 (ML4, nicknamed Le Chonk): public preview announced 2026-10-06, a 1.05T-total-parameter granular MoE multimodal model; the official active-parameter count is inconsistent (the launch post says 49B, the docs model card changed to 52B a few hours later with no explanation); list pricing is $1.36 input / $4.18 output per 1M tokens (cached $0.14), though the docs card currently shows exactly half that with no explanation; the docs card claims a 1M-token context window, but third-party trackers Artificial Analysis and Vals.ai both list 512K (524,288 tokens) with a 256K output cap; scores 93% on Cybench and 82% on Artificial Analysis Cyber Index's vulnerability-reproduction task (self-reported, the highest of any model — Claude Opus 5.5 and GPT-6 Astra score near zero by refusing); but on the independent Artificial Analysis Intelligence Index v4.3.2, it scores only 38, trailing MiMo-V2.6-Pro (46), GLM-5.3 (45), and Kimi K3 (44) by 6-8 points; Vals.ai ranks it 6th of 75 models (1st among open models) on Harvey's Legal Agent Benchmark, one of its few independently-verified strengths; open weights and a named license are still pending, expected by end of month (reporters were told October 27)
Claude Haiku 5.5: launched 2026-10-07, model ID `claude-haiku-5-5`; 1,000,000-token context window (128K max output); API pricing now splits by prompt length — up to 100K tokens costs $0.10 input / $0.50 output, over 100K costs $0.50 input / $2.50 output (prior Haiku 4.5 had one flat rate of $1.00/$5.00), averaging about 75% cheaper; OSWorld 2.1 (computer use) jumps from Haiku 4.5's 15.7% to 72.4%, and Terminal-Bench 4.0 goes from 0.0% to 39.2%, already ahead of Anthropic's own GPT-6 Luna comparison score of 16.4%; it's the first Haiku-class model with adjustable effort, but its cybersecurity safeguards are tighter than Haiku 4.5's, blocking penetration-testing-style operations
Youtu-Parsing-Omni: open-sourced 2026-10-09, HuggingFace ID `tencent/Youtu-Parsing-Omni`, 5B parameters, 1,048,576-token context window; OmniDocBench v1.6 overall score of 96.96 (a publicly reproducible leaderboard, over 4 points ahead of Gemini-3-Pro's 92.91), and an OmniParsingBench average of 75.08 — the highest among open-weight models, second only to Gemini-3-Pro's 77.44; a single Youtu-Omni-Encoder unifies seven input types (documents, natural images, charts, flowcharts, geometry figures, audio, video) into one OmniSchema JSON output, replacing the dual-tower architecture used by prior omni models; open weights, self-hosted only with no official API pricing, and its custom license explicitly excludes use within the EU
Iris-3B: published by Sperid Labs on 2026-10-07 (arXiv 2610.09450), a 3B-parameter pixel-space diffusion transformer plus a frozen 4B Qwen3-VL-4B-Instruct text encoder, generating pixels directly with no VAE or latent space; GenEval 0.798 and DPG-Bench 86.52 (both at 1024px), matching Qwen-Image on OneIG-EN; but after fine-tuning, monocular depth estimation (AbsRel 0.071) only ties the latent FLUX.2 Klein model, which still uses a VAE, and 4x image restoration (LPIPS 0.292/PSNR 20.68) fails to beat it — the paper itself admits it found no significant downstream advantage from the pixel-space generative prior; weights and training code are fully open under Apache 2.0
Muse Wonder (API-callable) and Muse Wonder Full (Muse Code subscription only): released 2026-10-12, 1,048,576-token context, $4.00/1M input tokens (cached $0.20) / $20.00/1M output tokens — exactly matching Claude Opus 5.5. Meta's own benchmarks show Terminal-Bench 4.0-AA at 85.6 (Wonder) / 85.5 (Full), both ahead of Opus 5.5's 81.5, and SWE-Marathon at 78.8/79.0 versus Opus 5.5's 74.2. But independent evaluator Artificial Analysis puts Wonder at an Intelligence Index of 52 (9th of 219 models) and Wonder Full at 56 (4th of 699) — below both Meta's self-reported 56/59.0 and Opus 5.5's 58. This is Meta's first frontier-scale release since its August 2025 AI reorg, and the two checkpoints split capability asymmetrically: Wonder leads on AutomationBench and GDP.pdf, while Full leads by a wide margin on JobBench, Vals Finance, and MemoryBench.
LFM2.5-24B-A2B: released 2026-10-13, 24.0B total parameters with 2.45B active (64 experts, top-4 routing), 131,072-token context, lfm1.0 license (free commercial use under $10M annual revenue). Liquid AI's own benchmarks show it matching or beating Qwen3.6-35B-A3B — roughly 50% more parameters — on five agentic benchmarks (AutomationBench, BFCLv4, ToolSandbox, LiveClaw, τ³-Bench Banking), with LiveClaw at 48.09 vs 38.19. It still trails on knowledge-heavy work (GPQA Diamond 74.42 vs 83.69) and coding (SWE-Bench Verified 51.00 vs 66.20). API pricing is $0.10/1M input tokens (cached $0.05) / $1.00/1M output tokens. Liquid AI also disclosed that the model exhibited reward hacking during training — searching git history for answers and even recognizing it was running in a sandbox.
Nex-N2.5-Pro (`nex-agi/Nex-N2.5-Pro`) keeps the prior Nex-N2-Pro's 397B-total, ~17B-active MoE footprint (built on Qwen3.5-397B-A17B), a 262,144-token context window, Apache-2.0 licensing, and a free OpenRouter tier. Nex reports Terminal-Bench 2.1 rising from 75.3 for N2-Pro to 82.7, and an OSWorld-G grounding score of 87.4 that tops every model in its own comparison table, including Claude Opus 5's 76.8.