2026 Q1 Open-Source LLM Landscape: From Frontier Models to On-Device, a Complete Survey

🌏 中文版

The pace of open-source LLM progress in Q1 2026 is too fast for any single article to cover completely. In Q1 alone, over 15 major models were released — frontier-tier MoE models broke through 1T parameters, mid-tier efficiency improved dramatically (Qwen3.5-35B achieves better performance than the previous-gen 235B flagship with only 3B active parameters), and mobile small models were compressed down to 140M for on-device inference.

This article is a navigation map. For each category, I’ll lay out the current landscape and key metrics, with details linked to dedicated articles — those cover the full technical architecture, benchmark comparisons, and hands-on experience.

2026 Q1 Open-Source Model Release Timeline

Let’s start with the big picture. Here are the major open-source model releases from January to March 2026:

Date	Model	Developer	Highlights
01/15	InternLM3-8B	Shanghai AI Lab	8B, 4x data efficiency vs Llama 3.1
01/27	Kimi K2.5	Moonshot AI	1T/32B MoE, Agent Swarm, MIT
02/11	GLM-5	Zhipu AI	745B/44B MoE, MIT, #1 open-source
02/11	MiniMax-M2.5	MiniMax	230B/10B MoE, SWE-bench 80.2%
02/16	Qwen3.5-397B	Alibaba	397B/17B MoE, native multimodal, Apache 2.0
02/24	Qwen3.5 Medium	Alibaba	122B/35B/27B three sizes
Feb	Tiny Aya	Cohere	3.35B, 70+ languages
03/01	Qwen3.5 Small	Alibaba	0.8B—9B, mobile multimodal
03/15	GLM-5-Turbo	Zhipu AI	Accelerated version of GLM-5
03/16	Mistral Small 4	Mistral AI	119B/6.5B MoE, unified reasoning + multimodal + code
03/26	Voxtral TTS	Mistral AI	Open-source speech synthesis, 9 languages
03/27	GLM-5.1	Zhipu AI	Improved GLM-5, code ability approaching Claude Opus

Frontier Open-Source Models (100B+)

Nearly all frontier open-source models in 2026 use the Mixture of Experts (MoE) architecture — massive total parameter counts, but only a small fraction activated per inference, striking a balance between performance and cost. Chinese labs dominate this tier.

GLM-5 / GLM-5.1 (Zhipu AI) — Highest-Ranked Open-Source

745B total parameters, 44B active parameters (256 experts, 8 activated per token), MIT license. Trained entirely on Huawei Ascend chips, without a single NVIDIA GPU. Artificial Analysis Intelligence Index #1 open-source (50 points), SWE-bench Verified 77.8%, Humanity’s Last Exam 50.4%. API pricing around $1.00/$3.20 per M tokens.

GLM-5.1, released on March 27, further improved performance, with code ability approaching Claude Opus 4.6.

-> GLM-5 Deep Dive: Zhipu AI’s 744B Open-Source Model

Kimi K2.5 (Moonshot AI) — 1T Parameters + Agent Swarm

A 1T total / 32B active parameter MoE model, MIT license (attribution required for >100M MAU or >$20M monthly revenue). Native multimodal, with the biggest highlight being Agent Swarm — capable of coordinating 100 sub-agents simultaneously and issuing 1,500 tool calls. Code and math are open-source strongest on some benchmarks.

-> Kimi Deep Dive: Moonshot AI’s Long-Context AI Model

Qwen3.5-397B-A17B (Alibaba) — Multimodal Flagship

397B total / 17B active parameters, hybrid MoE (Gated Delta Networks + sparse MoE, 512 experts, 10 routed + 1 shared per token). 262K context (expandable to 1M), native multimodal (text/image/video), supports 201 languages, Apache 2.0 license.

After the Qwen3 series (235B flagship) in April 2025, Alibaba released the fully upgraded 3.5 series in just half a year — the evolution speed is remarkable.

MiniMax-M2.5 — SWE-bench Rivaling Claude Opus

230B total / 10B active parameters MoE model, Modified MIT license. SWE-bench Verified 80.2% (matching Claude Opus 4.6), Multi-SWE-Bench #1 (51.3%), at 1/20th the cost of Claude Opus. Trained with their proprietary Forge RL framework.

MiniMax is relatively low-profile, but M2.5’s code ability is among the very best in open-source models.

DeepSeek V3 / R1 — Still Important but Starting to Age

DeepSeek-V3 (671B/37B MoE, 2024/12) and R1 (reasoning-specialized, 2025/01) were open-sourced under MIT, with training cost of only $5.6M, sending shockwaves through the industry. V3.1 (2025/08) merged V3 and R1 capabilities with hybrid thinking mode. V3.2-Exp (2025/09) introduced Sparse Attention.

But as of 2026/03/31, neither R2 nor V4 have been released — the originally planned May 2025 schedule has been delayed multiple times. CEO Liang Wenfeng reportedly wasn’t satisfied with R2’s performance and may be retraining on Huawei chips.

Llama 4 Scout / Maverick (Meta) — Ultra-Long Context

Released April 2025. Scout (109B/17B MoE, 16 experts) has a 10M context window, Maverick (400B/17B MoE, 128 experts) has 1M context. Llama 4 Community License (open-weight but not fully open-source).

Multimodal (text + image input), multilingual support, but Chinese capability still lags behind Qwen and GLM.

gpt-oss-120b (OpenAI) — First Open-Source Model

Released August 2025, 117B total / 5.1B active parameters MoE, Apache 2.0 license. Reasoning capability close to o4-mini, can run on a single 80GB GPU. Also released gpt-oss-20b (21B/3.6B).

OpenAI releasing an open-source model is itself a historic event, even though it’s no longer the most capable.

Devstral 2 (Mistral) — Code-Specialized

123B dense model, 256K context, Modified MIT. SWE-bench Verified 72.2%, 7x more cost-efficient than Claude Sonnet. Designed specifically for code generation and agentic coding.

Frontier Tier Overview

Model	Total Params	Active Params	Architecture	License	Released	Strength
GLM-5.1	745B	44B	MoE	MIT	2026/03	#1 overall open-source
Kimi K2.5	1T	32B	MoE	MIT*	2026/01	Agent Swarm
Qwen3.5-397B	397B	17B	MoE	Apache 2.0	2026/02	Multimodal + multilingual
MiniMax-M2.5	230B	10B	MoE	Modified MIT	2026/02	Best SWE-bench
DeepSeek-V3.1	671B	37B	MoE	MIT	2025/08	Lowest cost
Llama 4 Maverick	400B	17B	MoE	Llama 4	2025/04	1M context
Llama 4 Scout	109B	17B	MoE	Llama 4	2025/04	10M context
gpt-oss-120b	117B	5.1B	MoE	Apache 2.0	2025/08	OpenAI open-source
Devstral 2	123B	123B	Dense	Modified MIT	2025/12	Code-specialized

Mid-Tier Models (7B—70B)

Not every application needs a frontier model. 7B—70B models can run on a single machine or a few GPUs, hitting the sweet spot for many production scenarios.

Qwen3.5 Medium Series (2026/02)

Alibaba released three sizes on February 24, all Apache 2.0 with native multimodal:

Model	Total Params	Active Params	Architecture	Highlights
Qwen3.5-122B-A10B	122B	10B	MoE	Best agentic benchmark (BFCL-V4 72.2)
Qwen3.5-35B-A3B	35B	3B	MoE	Surpasses previous-gen 235B flagship
Qwen3.5-27B	27B	27B	Dense	SWE-bench Verified 72.4, matching GPT-5 mini

Qwen3.5-35B-A3B is particularly noteworthy — 3B active parameters surpassing the previous-gen flagship with 22B active parameters represents a massive leap in architectural efficiency.

Mistral Small 4 (2026/03)

119B total / 6.5B active parameters MoE, 256K context, Apache 2.0. Unifies Magistral (reasoning), Pixtral (multimodal), and Devstral (code) into a single model with adjustable reasoning intensity.

Gemma 3 (Google, 2025/03)

Based on Gemini 2.0 technology, available in 1B/4B/12B/27B sizes, multimodal (text + image), 128K context. Traditional Chinese performance on Cloudflare Workers AI outperforms Llama.

-> Gemma 3 on Cloudflare Workers AI: A Pragmatic Choice for Traditional Chinese Applications

Devstral Small 2 (Mistral, 2025/12)

24B dense, Apache 2.0. SWE-bench Verified 68.0%, runs on consumer hardware. If you only need code capability and hardware is limited, this is the best value option.

InternLM3-8B (Shanghai AI Lab, 2026/01)

8B parameters, 4x data efficiency vs Llama 3.1 (trained on 4T tokens), integrates conversational and deep thinking modes, performance matching GPT-4o-mini.

Other Widely Used Models

• Llama 3.1 70B / 3.3 70B (2024): Most mature English ecosystem, 128K context
• Qwen3-32B / 14B / 8B (2025/04): Apache 2.0, strong in Chinese and multilingual

Mobile Small Models (Below 7B)

1B—4B parameter models, after quantization, can achieve usable inference speeds on regular smartphones. The most important Q1 2026 advances are the Qwen3.5 Small series and continuously improving inference frameworks.

Qwen3.5 Small Series (2026/03)

0.8B / 2B / 4B / 9B four sizes, hybrid architecture (Gated DeltaNet + MoE), 262K context, native multimodal (4B and above), 201 languages, Apache 2.0. The 9B version even surpasses the previous-gen Qwen3-30B.

The top choice for Traditional Chinese and multilingual on-device scenarios.

Gemma 3n (Google, 2025/05—07)

Designed specifically for mobile, Per-Layer Embeddings (PLE) let a 5B parameter model occupy only 2GB RAM. E2B/E4B two sizes, multimodal (text/image/audio/video), optimized in partnership with Qualcomm, MediaTek, and Samsung.

Other Notable Small Models

Model	Parameters	License	Strength
Llama 3.2	1B / 3B	Llama Community	Most mature English ecosystem, 128K context
Phi-4-mini-flash-reasoning	3.8B	MIT	Math reasoning, 10x throughput
SmolLM3	3B	Apache 2.0	Fully open-source (including training data), 128K context
MobileLLM-R1	140M—950M	MIT	Best sub-billion reasoning
Tiny Aya	3.35B	CC-BY-NC	70+ languages, edge devices
gpt-oss-20b	21B / 3.6B active	Apache 2.0	OpenAI small open-source model

-> Mobile Small Models Complete Comparison: Choices and Constraints in 2026

Embedding Models: The Foundation of RAG

RAG requires not just generation models but also Embedding models to vectorize text. This domain changed significantly in 2026 — Qwen3-Embedding took #1 on MTEB multilingual, Jina v4 supports multimodal embeddings, and Nomic v2 became the first to apply MoE to embedding models.

Model	Developer	Params	Dimensions	Max Tokens	Multilingual	License	Strength
Qwen3-Embedding-8B	Alibaba	8B	7168	32K	100+ languages	Apache 2.0	MTEB multilingual #1 (70.58)
BGE-M3	BAAI	568M	1024	8K	100+ languages	MIT	Only model supporting dense + sparse + ColBERT tri-mode
Jina Embeddings v4	Jina AI	3.8B	2048	32K	30+ languages	CC-BY-NC-4.0	Multimodal (text + image + PDF)
NV-Embed-v2	NVIDIA	7.85B	4096	32K	Primarily English	CC-BY-NC-4.0	High MTEB English score
Nomic Embed v2	Nomic AI	475M (MoE)	768	512	~100 languages	Apache 2.0	First MoE Embedding, fully open-source
EmbeddingGemma-300M	Google	300M	768	2K	100+ languages	Gemma	Edge deployment, <200MB RAM

How to choose: For Traditional Chinese RAG, use BGE-M3 (MIT, tri-mode retrieval) or Qwen3-Embedding (highest accuracy). For multimodal embedding (images + PDF), use Jina v4. For edge devices, use EmbeddingGemma.

-> BGE-M3: Why This Embedding Model Fits Traditional Chinese RAG

Reranker Models: Boosting Retrieval Precision

Embedding handles recall; Reranker handles precision ranking. A good Reranker can dramatically improve RAG answer quality.

Model	Developer	Params	License	Strength
Qwen3-Reranker (0.6B/4B/8B)	Alibaba	0.6B—8B	Apache 2.0	Full pipeline with Qwen3-Embedding
BGE Reranker v2-m3	BAAI	568M	MIT	Pairs with BGE-M3, most permissive license
Jina Reranker v3	Jina AI	0.6B	CC-BY-NC-4.0	131K context, cross-document interaction
gte-reranker-modernbert-base	Alibaba	149M	Apache 2.0	149M matches 1.2B Nemotron

Most pragmatic combinations: BGE-M3 + BGE Reranker v2-m3 (all MIT) or Qwen3-Embedding + Qwen3-Reranker (all Apache 2.0).

-> Cross-Encoder Reranking: Getting the Most Relevant Documents to the Top -> ColBERT: The Third Path for Vector Search -> SPLADE: Smarter Sparse Vector Search Than BM25

Code Models: Specialized for Writing Code

Most frontier LLMs can write code, but some models are specifically optimized for coding.

Model	Developer	Params	License	Highlights
Qwen3-Coder-480B-A35B	Alibaba	480B/35B MoE	Apache 2.0	Open-source coding flagship
Qwen2.5-Coder-32B	Alibaba	32B	Apache 2.0	HumanEval 92.7% (surpasses GPT-4o)
Devstral 2	Mistral	123B dense	Modified MIT	SWE-bench 72.2%, 256K context
Devstral Small 2	Mistral	24B	Apache 2.0	SWE-bench 68%, runs on consumer hardware
StarCoder2-15B	BigCode	15B	OpenRAIL-M	600+ programming languages, widest coverage
DeepSeek-Coder-V2	DeepSeek	236B/21B MoE	DeepSeek License	128K context, code + math

Speech Models: STT and TTS

Speech-to-Text (STT)

Model	Developer	Params	WER	Languages	License
Canary Qwen 2.5B	NVIDIA	2.5B	5.63%	English	CC-BY-4.0
Granite Speech 3.3 8B	IBM	~9B	5.85%	Multilingual + translation	Apache 2.0
Whisper Large V3	OpenAI	1.55B	7.4%	99+ languages	MIT
Whisper Large V3 Turbo	OpenAI	809M	7.75%	99+ languages	MIT
Parakeet TDT 1.1B	NVIDIA	1.1B	~8%	English	CC-BY-4.0

For multilingual, go with Whisper V3 (MIT, 99+ languages). For highest English accuracy, use Canary Qwen. For real-time streaming, use Parakeet TDT (>2000x real-time speed).

Text-to-Speech (TTS)

Model	Developer	Params	Languages	License	Strength
Voxtral TTS	Mistral	4B	9 languages	Apache 2.0	3-second voice cloning, 70ms latency, released 2026/03
Kokoro	hexgrad	82M	Multilingual	Apache 2.0	High-quality synthesis at 82M, runs on CPU
Fish Speech V1.5	Fish Audio	—	Chinese/English multilingual	—	300K hours training, DualAR architecture
Parler TTS	Hugging Face	~600M	English	Apache 2.0	Prompt-controllable tone and style

Voxtral TTS is the standout release of 2026/03 — 3 seconds of audio is all it needs to clone a voice, supports streaming, Apache 2.0 license.

Image Generation Models

Model	Developer	Params	License	Strength
FLUX.2 Dev	Black Forest Labs	32B	Open weights (non-commercial)	Best text rendering and characters
FLUX.2 Klein 4B	Black Forest Labs	4B	Apache 2.0	Consumer GPU instant generation
Stable Diffusion 3.5 Large	Stability AI	8.1B	Free for revenue <$1M	Runs on 12GB VRAM
SDXL	Stability AI	~3.5B	CreativeML Open RAIL-M	Most mature ecosystem (LoRA, ControlNet)

Video Generation Models

Model	Developer	Params	License	Strength
Wan 2.2	Alibaba	5B/14B	Apache 2.0	Cinema-grade quality, 5B runs on consumer GPU
HunyuanVideo	Tencent	13B	Tencent License	Quality rivaling Runway Gen-3
Open-Sora 2.0	HPC-AI Tech	11B	Open-source	Training cost only $200K, approaching OpenAI Sora
Mochi 1	Genmo	10B	Apache 2.0	Commercial-friendly

Open-source video generation made the most dramatic progress in 2026 — Wan 2.2 and HunyuanVideo quality directly rivals Sora and Veo.

Deployment and Inference: How to Run the Models

After choosing a model, you still need to choose how to run it. The 2026 inference ecosystem is quite mature, but optimal choices vary significantly by scenario.

Local Development -> Ollama

Download and launch models with a single command, Docker-style CLI + OpenAI-compatible API. Ideal for personal development, prototyping, and offline use. Not suitable for high-concurrency production environments.

-> Ollama Complete Guide: Run LLMs Locally with One Command

Production Deployment -> vLLM

PagedAttention + continuous batching + prefix caching — currently the most mainstream open-source LLM inference engine. Ideal for API services requiring high throughput.

-> vLLM: From PagedAttention to Production-Grade LLM Inference Engine

Edge / Serverless -> Cloudflare Workers AI

If you don’t want to manage GPUs, Cloudflare Workers AI provides zero-ops inference services. Model selection is limited, but Gemma 3 12B outperforms Llama for Traditional Chinese.

-> Gemma 3 on Cloudflare Workers AI: A Pragmatic Choice for Traditional Chinese Applications

Deployment Quick Reference

What do you need?
├── Local experiments / prototyping -> Ollama
├── Production API service         -> vLLM (self-managed GPU) or Cloudflare Workers AI (managed)
├── Mobile app                     -> llama.cpp + GGUF or Google AI Edge
└── Offline / privacy              -> Ollama or on-device models

Leaderboard Status (2026/03)

The gap between open-source and closed-source models is closing rapidly, though frontier closed-source models still lead.

LMArena (formerly Chatbot Arena)

5,632,160 votes, 333 models. Top 9 are all closed-source (Claude Opus 4.6 leads at 1504 Elo), with GLM-5 family and Kimi K2.5 as the highest-ranked open-source models.

Artificial Analysis Intelligence Index

314 models. Highest closed-source score: 57 (Gemini 3.1 Pro Preview), highest open-source score: 50 (GLM-5 Reasoning) — the gap has narrowed from 20+ points a year ago to just 7 points.

Fastest model: Mercury 2 (789.2 tok/s). Cheapest model: Gemma 3n E4B ($0.03/M tokens).

How to Choose a Model: Decision Framework

Step 1 — Identify Your Use Case

Scenario	Recommended Direction
General conversation / Chatbot	GLM-5.1, Kimi K2.5, Qwen3.5-397B
Code generation / Agentic Coding	MiniMax-M2.5, GLM-5.1, Devstral 2
Traditional Chinese RAG	Gemma 3 12B (generation) + BGE-M3 (Embedding)
Multilingual applications	Qwen3.5 series (201 languages)
Mobile offline	Gemma 3n, Qwen 3.5 Small
Math / Reasoning	Phi-4-mini (mobile), DeepSeek-R1 (cloud)
Ultra-long text	Llama 4 Scout (10M context), Kimi K2.5
Extremely low budget	Cloudflare Workers AI (free tier) + Gemma 3

Step 2 — Identify Your Constraints

• GPU resources: No GPU -> Ollama CPU mode or Cloudflare Workers AI; Have GPU -> vLLM
• Language needs: Traditional Chinese -> Qwen or Gemma first; English -> Llama or Phi first
• License needs: Commercial use -> MIT / Apache 2.0 (GLM-5, Qwen3.5, SmolLM3); watch out for Llama and Modified MIT restrictions
• Privacy needs: Can’t send to cloud -> local deployment or on-device

Step 3 — Just Try It

Benchmarks and real-world performance don’t always align. Running a test with your own data is more useful than reading any leaderboard. Ollama lets you evaluate a model in five minutes — the barrier is too low to have any excuse not to try.

How to Track the Latest Models

Open-source models iterate extremely fast, and by the time this article is published, there might already be something new next week. Here are the channels I regularly follow:

Leaderboards / Comparison Sites

• Artificial Analysis: Independent measurement, 72-hour update cycle, 314+ models, includes speed (tokens/sec) and price comparisons, filterable by model size — especially useful for tracking cost-effectiveness
• LiveBench: Monthly new questions from latest arXiv papers and news, avoids benchmark gaming, covers math, code, and reasoning
• LMArena (formerly Chatbot Arena): Crowd-sourced blind A/B comparisons producing Elo ratings. Closer to “how it actually feels to use” than benchmarks
• LiveBench: Monthly new questions from latest arXiv papers and news, avoids benchmark gaming

Real-Time Tracking

• Hugging Face Trending Models: Reflects what the community is downloading in real-time; new open-source models often appear here before they hit the news
• Hugging Face Daily Papers: Community-voted daily paper selections; technical papers for new models almost always make the list
• LLM Stats: Updates hourly, aggregates model release news from TechCrunch, VentureBeat, and other sources; shows new models from the past 24 hours
• Official blogs / announcement pages of each lab:
  • Google AI Blog · Meta AI Blog · Microsoft Research Blog
  • Qwen Blog · DeepSeek · Zhipu AI (GLM) · Moonshot AI (Kimi)
  • Mistral AI News · MiniMax · Stability AI
  • Hugging Face Blog · NVIDIA AI Blog · Jina AI Blog · BAAI (BGE)

Community

• r/LocalLLaMA: The most active local model community on Reddit; first-hand benchmarks, quantized versions, and hands-on reviews mostly originate here
• The comment sections on Hugging Face Daily Papers also frequently feature in-depth discussions between model authors and the community

Recommended strategy: Track new releases with LLM Stats -> check community reaction on Hugging Face Trending -> compare numbers on Artificial Analysis / LMArena -> read hands-on feedback on r/LocalLLaMA. But ultimately, you still need to test with your own data — benchmarks and real-world performance don’t always align.

The Big Picture

Five structural trends defined the open-source LLM landscape in Q1 2026:

1. MoE dominates: Nearly all frontier models are MoE, with active parameters controlled between 10—44B; inference cost no longer scales linearly with total parameter count
2. Chinese labs lead: GLM-5, Kimi K2.5, Qwen3.5, MiniMax-M2.5, DeepSeek — 4 out of the top 5 frontier open-source models come from China
3. Native multimodal: Qwen3.5, Gemma 3n, Kimi K2.5 all integrate vision from pre-training, no longer bolted-on adapters
4. MIT/Apache 2.0 as standard: Frontier open-source model licenses are becoming increasingly permissive, dramatically lowering the commercial use barrier
5. Open-source catching up with closed-source: GLM-5 scored 50 on Artificial Analysis, closed-source highest is 57 — a year ago the gap was over 20 points

The biggest structural shift: Model selection decisions are moving from “open-source vs closed-source” to “self-hosted vs managed.” Technical capability is no longer the bottleneck — operational capability is.

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References

In-site articles:

• GLM-5: Zhipu AI’s 744B Open-Source Model
• Kimi: Moonshot AI’s Long-Context AI Model
• Mobile Small Models: Choices and Constraints in 2026
• BGE-M3: Why This Embedding Model Fits Traditional Chinese RAG
• Ollama Complete Guide: Run LLMs Locally with One Command
• vLLM: From PagedAttention to Production-Grade LLM Inference Engine
• Gemma 3 on Cloudflare Workers AI: A Pragmatic Choice for Traditional Chinese Applications

External resources:

• GLM-5 — Hugging Face
• Kimi K2.5 — Hugging Face
• Qwen3.5-397B-A17B — Hugging Face
• MiniMax-M2.5 Official
• DeepSeek Models Guide — BentoML
• Llama 4 — Meta AI
• gpt-oss — OpenAI
• Mistral Small 4 — Mistral AI
• Devstral 2 — Mistral AI
• LMArena Leaderboard
• Artificial Analysis LLM Leaderboard
• LiveBench
• LLM Stats — AI Model Releases
• r/LocalLLaMA — Reddit