GLM-5 from Zhipu AI leads all open-weight models with a BenchLM score of 85 and 77.8% on SWE-bench Verified, trailing GPT-5.4 by just 9 points. Two years ago, that gap was 30 points. Models like Llama 4, Mistral Large, and DeepSeek-V4 challenge the proprietary dominance of Claude 4.5 and GPT-5.1, and on specific tasks, they win outright. This isn't about "almost as good" anymore. It's about which open-source AI models you should actually deploy in production, and where closed models still justify their price tags.
| Category | Best Open Model | Score | Best Closed Model | Score | Gap |
|---|---|---|---|---|---|
| General Benchmarks | GLM-5 | 85 (BenchLM) | GPT-5.4 / Claude 4.5 | ~94 | 9 points |
| Coding (SWE-bench) | DeepSeek-V4: 71.8% | 71.8% | Claude 4.5: 77.2% | 77.2% | 5.4% |
| Reasoning (ARC-AGI-2) | Mistral Large 2: 27.4% | 27.4% | Gemini 3: 31.1% | 31.1% | 3.7% |
| Math (MATH-500) | DeepSeek R1: 97.3% | 97.3% | OpenAI o1 | ~97% | Tied |
| Pricing (per 1M tokens) | DeepSeek R1: $0.55 | $0.55 | Claude Opus | ~$15 | 27x cheaper |
| Context Window | Qwen 3.6 Plus: 1M tokens | 1M | Gemini 3.1 Pro | 2M | Closed wins |
For most enterprise tasks—summarization, code generation, customer support—open-source models now deliver comparable quality. The 5-10% gap only matters for frontier research or high-stakes applications.
Chinese labs—DeepSeek, Moonshot AI, Zhipu AI, and Alibaba—hold most of the top positions among open weight models. This represents a complete reversal from 2024, when Meta's Llama dominated the conversation.
DeepSeek V4 Pro (Max) leads BenchLM.ai's open weight leaderboard at 87 overall, followed by Kimi 2.6 at 86, GLM-5 (Reasoning) and GLM-5.1 at 83, and Qwen3.5 397B (Reasoning) at 79. These aren't just impressive for open models. In April 2026, DeepSeek V4 matches or exceeds GPT-4o on 7 of 12 standard benchmarks.
DeepSeek first captured global attention in December 2024, when it released its V3 large language model trained on just $5.6 million worth of processors, which AI researcher Andrej Karpathy called a "joke of a budget." The April 2026 V4 release continues that trajectory. The company claims its new V4-Pro-Max model outperforms its opensource peers across reasoning benchmarks, and outstrips OpenAI's GPT-5.2 and Gemini 3.0 Pro on some tasks.
The strategic picture matters here. China's leading AI labs open-source their best models for strategic reasons: ecosystem building creates dependency, when thousands of companies build on DeepSeek V4, those companies contribute back improvements, report bugs, and create tooling that benefits the core model. This approach has produced genuine results, not marketing vaporware.
| Model | Parameters | Key Strength | Benchmark Highlight | License | Best Use Case |
|---|---|---|---|---|---|
| DeepSeek V4 Pro | 284B total / 13B active | Cost efficiency | Matches GPT-4o on 7/12 benchmarks | MIT | Production reasoning at scale |
| DeepSeek R1 | 671B MoE | Math reasoning | 97.3% MATH-500 | MIT | Complex problem-solving |
| Qwen 3.6 Plus | Closed weights | Context length | 1M token context, 61.6% Terminal-Bench | Proprietary | Large codebase analysis |
| Qwen3-235B | 235B / 22B active | General intelligence | Top open-weight generalist, ahead of Llama 4 | Apache 2.0 | Enterprise general-purpose |
| Mistral Large 3 | 675B total / 41B active | Multilingual | Particularly good at French, German, Spanish, Arabic | Apache 2.0 | European/multilingual apps |
| GLM-5 | 744B / 40B active | Coding | 85 BenchLM, 77.8% SWE-bench | MIT | Agentic coding tasks |
| Llama 4 Maverick | 400B / 17B active | Ecosystem | 128-language generation | Llama License | Community tooling, fine-tuning |
DeepSeek-V3 activates just 37B of its 671B parameters per token, scoring 88.5% on MMLU and leading open models on HumanEval. This mixture-of-experts architecture is what makes 671B models financially viable to run in production.
Qwen deserves special attention. Qwen2.5-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, and Alibaba has released an entire family from tiny 0.6B edge models to massive 480B coding specialists. Qwen3-4B rivals Qwen2.5-7B performance at roughly half the memory footprint.
The gap hasn't closed everywhere. The models seem to fall slightly behind frontier models in knowledge tests, specifically OpenAI's GPT-5.4 and Google's latest Gemini 3.1 Pro, with a "developmental trajectory that trails state-of-the-art frontier models by approximately 3 to 6 months".
Claude Opus 4.6, GPT-5.4 Pro, and Gemini 3.1 Pro Deep Think retain a meaningful lead on reasoning-heavy benchmarks like GPQA Diamond, Humanity's Last Exam, and frontier math, typically by 3-8 percentage points. That 3-8% matters enormously for medical diagnosis, legal analysis, or safety-critical applications.
Closed models also dominate on polish and ecosystem. Closed models offer polished ecosystems: Claude's Artifacts and Projects, GPT's plugins and custom GPTs integrate seamlessly with existing workflows, reducing development time. For rapid prototyping, closed models win; for long-term customization, open-source prevails.
Closed-model APIs come with SLAs, uptime guarantees, and enterprise support. Self-hosted open models come with GitHub issues and community forums. For production applications where downtime has direct revenue impact, this difference matters.
Multimodal capabilities still favor closed providers. Both V4 Flash and V4 Pro support text only, unlike many of its closed-source peers, which offer support for understanding and generating audio, video, and images. If your application needs native image understanding or video generation, you're still looking at GPT-5, Gemini, or Claude.
Self-hosting math changes everything at scale. Self-hosting breaks even at roughly 2M+ tokens/day versus API access. Below that threshold, APIs are typically cheaper after accounting for full infrastructure costs.
A single H100 on spot instances at $1.65/hour with 70% utilization processes tokens at approximately $0.004 per thousand—compared to $6.25/1K for GPT-4o, representing a 1,500x cost reduction at sufficient scale. That's not a typo. At high volume, self-hosting becomes dramatically cheaper.
For teams below the break-even threshold, managed inference providers offer a middle ground. New managed services (Together AI, Fireworks, Replicate) offer a middle ground: you deploy open-source models on their infrastructure with data privacy guarantees, paying per-token but avoiding hardware management. Pricing is typically 50-80% cheaper than equivalent closed model APIs.
DeepSeek R1 delivers GPT-4-class reasoning for $0.55 per million input tokens—27 times cheaper than Claude Opus—while Llama 4 Maverick outperforms GPT-4o on major benchmarks at just $0.30 per million tokens. Even through third-party APIs, open models cost a fraction of closed alternatives.
Hardware requirements vary widely. Smaller models like Mistral Small 4 (24B parameters) run on a single high-end consumer GPU. Larger models like Qwen3.5 397B and GLM-5 require multi-GPU setups or cloud instances with 4-8 A100/H100 GPUs. A single A100 GPU can run a 70B parameter model with acceptable latency for internal tools. For larger 405B models, quantization (e.g., 4-bit) reduces memory requirements from ~800GB to ~200GB.
The adoption numbers tell the story. Chinese open-weight providers now account for over 45% of OpenRouter traffic, with Xiaomi's MiMo V2 Pro alone moving 4.79T tokens per week. That inversion from under 2% a year ago changes how agencies should think about model selection.
According to Epoch AI, open-weight models now trail the SOTA proprietary models by only about three months on average. As open-source LLMs close the gap with proprietary ones, real differentiation now comes from how well you adapt the model and inference pipeline to your product.
Fine-tuning has become the real competitive moat. You can fine-tune Llama 4 on your proprietary data using LoRA or QLoRA, adapting its behavior to your domain—legal jargon, product catalogs, or internal codebases. The result is a model that understands your context intimately, often outperforming generic closed models on specific tasks.
Data sovereignty drives enterprise adoption. Privacy concerns have become the primary driver for open source adoption in regulated industries. Closed models require sending data to external servers, creating compliance challenges for healthcare, finance, and government applications. The European Union's AI Act implementation in 2025 has accelerated this trend.
The licensing landscape matters more than most teams realize. Mistral uses Apache 2.0 (fully permissive for commercial use). Meta's Llama license restricts use above 700M monthly active users. Many Chinese open weight models have custom licenses that may restrict commercial deployment in certain jurisdictions. Read the license before deploying in production.
For most production applications: Start with Llama 4 70B. It's the most versatile, best-supported, and easiest to deploy. If you hit its limits on reasoning tasks, try DeepSeek. If you need multilingual, try Mistral.
For coding agents: A Chinese open-source model claimed the top score on SWE-Bench Pro, beating GPT-5.4 and Claude Opus 4.6 on the hardest software engineering benchmark in AI. That model is GLM-5.1. For coding specifically, Kimi K2.6 sits just under Claude Opus 4.6 at 80.2% on SWE-Bench Verified with 58.6% on SWE-Bench Pro.
For massive context windows: Qwen 3.6 Plus's 1M token context window separates it. For the majority of production coding tasks under 100K tokens, all four models have sufficient context capacity. Where Qwen 3.6 Plus becomes the only viable option: monorepo analysis across hundreds of files, large-scale legacy codebase refactoring.
For budget-constrained high volume: Use Mistral 12B at $321/month self-hosted with acceptable quality (74% MMLU) for customer-facing chat, summarization, classification. Not for reasoning-heavy tasks, but unbeatable on cost per token.
For research and experimentation: DeepSeek R1's reasoning capabilities and MIT license make it ideal. The R1 reasoning model achieves 97.3% on MATH-500—matching OpenAI's o1 at a fraction of the cost. The January 2025 R1-0528 update pushed AIME scores from 70% to 87.5%.
The model weights are free to download, but you pay for compute (GPU hosting, electricity, or cloud inference). Self-hosting breaks even around 2M tokens per day versus closed APIs. Below that, managed APIs for open models (Together AI, Fireworks) are typically 50-80% cheaper than GPT or Claude.
DeepSeek uses MIT license (fully permissive). Qwen3 uses Apache 2.0 (permissive). GLM-5 uses MIT. Meta's Llama restricts commercial use above 700M monthly active users. Always verify the specific model version's license before production deployment.
GLM-5 scores 85 on BenchLM, trailing GPT-5.4/Claude 4.5 (~94) by 9 points. DeepSeek V4 matches GPT-4o on 7 of 12 benchmarks. For coding, GLM-5.1 beats both on SWE-Bench Pro. For pure reasoning, they trail by 3-8 percentage points on frontier benchmarks.
A 7-14B model runs on a single RTX 4090 (24GB) with quantization. 70B models need an A100 (80GB) or H100. 400B+ models require 4-8 H100 GPUs or aggressive quantization (4-bit reduces 800GB to ~200GB). Use managed inference APIs if you lack GPU infrastructure.
Model weights are deterministic and auditable. The community independently audits major open models for backdoors; no credible evidence of compromise has been found as of April 2026. The real risks are licensing changes for future versions and potential supply chain disruption for model updates during geopolitical tensions.
A single H100 at $1.65/hr with 70% utilization costs ~$0.004 per 1K tokens versus $6.25/1K for GPT-4o. That's 1,500x cheaper at scale. But infrastructure, monitoring, and engineering time add overhead. Break-even is roughly 2M+ tokens daily or $50,000+ annual API spend.