A researched directory of notable LLMs and coding models across major providers, with release dates, strengths, weaknesses, and open-versus-closed status.
This page is designed as a practical reference for teams evaluating model choices. Release dates refer to public launch dates for a model family or notable variant, not every patch release. The open vs closed column is about weight access: some models are fully closed APIs, some are open-weight, and some sit in a more restricted commercial middle ground. Entries are shown with the most recent launches first so the page works as a current-market reference, not just a history list.
Topic hubs
The glossary captures the market map. The topic hubs below branch into practical buying, implementation, and tooling questions so the page feeds the wider model SEO cluster instead of standing alone.
Compare cost, latency, and quality trade-offs across API and open-weight options.
See how model decisions connect to Bedrock, Vertex AI, gateways, and managed stacks.
Move from model research into buyer-side comparisons and operational tooling.
Provider history
The current model landscape makes more sense when you see how providers moved: closed labs expanded into coding and multimodal workflows, while open-weight ecosystems like Llama, DeepSeek, Qwen, and Hugging Face-origin models kept raising the floor on what open models can do.
Anthropic became a default choice for repo-scale reasoning and long-context development work, especially in terminal-first workflows.
Pushed daily-use coding quality closer to Opus while keeping a lower-cost default tier.
Extended Anthropic’s premium reasoning tier for harder coding and agentic tasks.
Marked a major quality and speed step-change for coding and general reasoning.
xAI positioned Grok as both a consumer-facing assistant brand and a developer/API family, with one notable open-weight release in Grok-1.
Marked the later closed-model API phase with stronger agentic reasoning claims.
Released as an Apache 2.0 open-weight base model after the initial beta period.
Hugging Face matters less as one single frontier family and more as the home of influential open-model releases, community fine-tunes, and distribution.
Made the case for compact, long-context, multilingual open models in the 3B range.
Showed how alignment and post-training could make smaller open models highly competitive.
One of the most important early multilingual open LLM efforts.
Google split its model story between Gemini as the managed frontier family and Gemma as the open-weight developer path.
Extended the Gemini 2.5 line into a faster, more cost-sensitive production tier.
Brought stronger thinking and coding performance into the Gemini family.
Extended Google’s open-weight line with stronger multimodal and long-context capabilities.
Made very long context a practical buying criterion for production teams.
OpenAI set the pace for mainstream API-delivered frontier models, then extended into multimodal and coding-agent workflows.
Brought OpenAI back into explicit software-engineering-agent workflows with codex-1.
Refocused the family on long-context, API-first coding and instruction-following improvements.
Introduced the omni model direction with stronger multimodal performance and lower cost than GPT-4 Turbo.
Qwen grew into one of the most important open-weight ecosystems because it shipped broad size ranges, coder variants, and multilingual coverage quickly.
Extended the line with hybrid thinking and larger MoE options.
Strengthened Qwen’s position as a serious open-weight coding family.
Expanded the family into a broad open-weight lineup spanning general, coder, and math variants.
DeepSeek became one of the most important open-weight challengers by combining strong efficiency claims with serious reasoning and coding performance.
Updated the V3 line with stronger post-training and coding-oriented improvements.
Made DeepSeek central to the open reasoning-model discussion.
Showed that an open-weight MoE flagship could compete more directly with frontier closed models.
Raised attention around efficient MoE design and lower-cost inference economics.
Meta’s Llama family normalized the idea that top-tier open-weight models could shape the mainstream model market.
Improved the practical 70B class for teams that needed stronger open-weight reasoning without 400B-scale costs.
Extended the family into lighter text tiers and multimodal vision variants.
Added the 405B tier and pushed open-weight models further into frontier territory.
Established Llama as the most important mainstream open-weight family of its era.
Mistral mixed open-weight momentum with commercial model lines, especially for European buyers and code-focused teams.
Strengthened Mistral’s enterprise and flagship positioning against larger US labs.
Marked Mistral’s explicit push into code-generation models.
Helped make sparse MoE models a core part of the open-weight conversation.
Cohere stayed especially relevant for enterprise retrieval and business-document workflows rather than general consumer-assistant mindshare.
Positioned Cohere as a serious enterprise retrieval and business-workflow model vendor.
Glossary
This list is curated rather than exhaustive. It aims to cover the model families most useful for teams making real API, coding, open-weight, and platform decisions, including notable Hugging Face ecosystem entries, recent coding-model variants, and major open-weight challengers like DeepSeek and Qwen.
| Model | Provider | Release date | Description | Strengths | Weaknesses | Open vs closed |
|---|---|---|---|---|---|---|
| Claude Sonnet 4.6 | Anthropic | 2026-02-17 | Anthropic’s newer daily-use frontier tier for coding, agents, and professional work. | Very strong default for coding and agentic tasks. | Still premium versus many open-weight options. | Closed |
| Claude Opus 4.6 | Anthropic | 2026-02-05 | Anthropic’s premium reasoning tier for harder repo-scale and planning-heavy tasks. | High quality ceiling for complex reasoning and coding. | Easy to overspend if used as the default tier. | Closed |
| Grok 4.1 | xAI | 2025-11-17 | xAI’s later closed flagship focused on stronger reasoning and production API use. | Competitive frontier-style reasoning and agentic positioning. | Closed ecosystem and smaller enterprise footprint than some rivals. | Closed |
| SmolLM3 | Hugging Face | 2025-07-08 | Hugging Face’s compact multilingual long-context model in the 3B class. | Practical small-model deployment with strong long-context and multilingual positioning. | Not a direct substitute for frontier-scale reasoning models. | Open-weight |
| Gemini 2.5 Flash | 2025-06-17 | Google’s faster and usually cheaper production-tier member of the Gemini 2.5 family. | Better speed and price/performance balance than the flagship tier for many workloads. | Lower absolute reasoning ceiling than Gemini 2.5 Pro. | Closed | |
| Codex (codex-1) | OpenAI | 2025-05-16 | OpenAI’s software-engineering agent path built for coding tasks and cloud sandbox execution. | Strong OpenAI ecosystem fit and coding-agent workflow. | Access and pricing are product-path dependent, not one simple model line. | Closed |
| Qwen3 | Qwen | 2025-04-29 | Qwen’s newer family with hybrid thinking modes and larger MoE options. | Strong open-weight flexibility with modern reasoning features. | Deployment and benchmark interpretation still require hands-on evaluation. | Open-weight |
| GPT-4.1 | OpenAI | 2025-04-14 | OpenAI API-first family focused on coding, instruction following, and long context. | Very strong coding and long-context API workflows. | Closed model economics and vendor dependence remain. | Closed |
| Gemini 2.5 Pro | 2025-03-25 | Google’s thinking-oriented Gemini flagship for advanced reasoning and coding. | Strong reasoning, coding, and multimodal reach. | Managed platform complexity is higher than simpler API products. | Closed | |
| DeepSeek-V3-0324 | DeepSeek | 2025-03-24 | A stronger post-trained update to the DeepSeek-V3 line with notable coding and reasoning improvements. | Very competitive open-weight quality with stronger coding-oriented tuning than the original V3 launch. | Still a large model family that is costly to self-serve at scale. | Open-weight |
| Gemma 3 | 2025-03-10 | Google’s open-weight model family built to give developers a lighter-weight alternative to Gemini. | Open-weight flexibility with strong small-to-mid-size deployment options. | Not intended to fully replace top closed-model capability. | Open-weight | |
| DeepSeek-R1 | DeepSeek | 2025-01-20 | DeepSeek’s reasoning-focused open-weight family that became central to the reasoning-model conversation. | Strong reasoning, math, code, and open-weight mindshare. | Reasoning-style usage can still become expensive to serve at scale. | Open-weight |
| DeepSeek-V3 | DeepSeek | 2024-12-27 | DeepSeek’s flagship open-weight general model with strong frontier comparisons. | Very competitive open-weight quality and strong coding reputation. | Large-scale serving remains non-trivial for most teams. | Open-weight |
| Llama 3.3 70B | Meta | 2024-12-19 | A more practical high-quality Llama tier for strong open-weight deployment without 400B scale. | Good balance of quality and deployment practicality. | Still requires careful infra planning compared with closed APIs. | Open-weight |
| Qwen2.5-Coder | Qwen | 2024-11-12 | Qwen’s open-weight coding family designed specifically for software engineering workloads. | Strong coder specialization with broad size options and open deployment flexibility. | Still requires model-selection discipline and self-hosting or hosted open-model trade-offs. | Open-weight |
| Qwen2.5 | Qwen | 2024-10-16 | Alibaba’s broad open-weight family spanning many sizes and specialist variants. | Very wide size range, multilingual coverage, strong coder variants. | Family breadth can create selection and governance complexity. | Open-weight |
| Llama 3.2 | Meta | 2024-09-25 | Meta’s extension of the Llama family into lighter text tiers and multimodal variants. | Broader deployment range across edge, lighter text, and multimodal use cases. | Not the strongest Llama option for highest-end reasoning. | Open-weight |
| Mistral Large 2 | Mistral | 2024-07-24 | Mistral’s flagship commercial model line for enterprise-grade reasoning and code. | Strong multilingual and coding capability with enterprise appeal. | Not a straightforward open-weight option despite Mistral’s open-model reputation. | Restricted / commercial |
| Llama 3.1 405B | Meta | 2024-07-23 | Meta’s flagship open-weight frontier-scale model in the Llama 3.1 family. | Strong open-weight quality at very large scale. | Inference and serving costs are high in practice. | Open-weight |
| Claude 3.5 Sonnet | Anthropic | 2024-06-20 | Anthropic’s widely adopted mid-tier model that became a coding and reasoning default. | Excellent repo reasoning, strong cost-to-quality balance. | Closed access and output costs still matter in heavy usage. | Closed |
| Codestral | Mistral | 2024-05-29 | Mistral’s code-generation-focused model family built specifically for software engineering tasks. | Clear coding specialization and strong relevance for code-heavy workflows. | Licensing and access are more constrained than a simple fully open-weight release. | Restricted / commercial |
| GPT-4o | OpenAI | 2024-05-13 | OpenAI’s omni flagship for text, image, audio, and real-time multimodal workflows. | Strong multimodal performance, fast responses, mature ecosystem. | Still premium-priced at scale and fully vendor-managed. | Closed |
| DeepSeek-V2 | DeepSeek | 2024-05-01 | DeepSeek’s MoE family that raised expectations around efficient open-weight inference. | Efficiency story and strong coding-oriented attention from developers. | Now mainly relevant as a history milestone, not the default latest recommendation. | Open-weight |
| Cohere Command R+ | Cohere | 2024-04-04 | Cohere’s business and retrieval-oriented flagship built for enterprise RAG-style workloads. | Strong fit for enterprise retrieval and internal-data use cases. | Less general market mindshare than OpenAI, Anthropic, or Google. | Closed |
| Grok-1 (open release) | xAI | 2024-03-17 | xAI’s early Grok base model released as open weights under Apache 2.0. | Useful historical example of a major lab releasing an open-weight base model. | Not the practical default choice for most current production teams. | Open-weight |
| Gemini 1.5 Pro | 2024-02-15 | Google’s long-context milestone model in the Gemini family. | Very large context and good multimodal platform integration. | Pricing and platform paths can be harder to model clearly. | Closed | |
| Mixtral 8x7B | Mistral | 2023-12-11 | Mistral’s sparse MoE model that helped popularize open MoE deployment economics. | Strong efficiency and good quality for its active-parameter profile. | Older than newer flagship open reasoning families. | Open-weight |
| Zephyr 7B Beta | Hugging Face H4 | 2023-10-25 | A Hugging Face alignment-focused fine-tune that showed how much post-training could improve smaller open models. | Strong instruct behavior for its size and highly instructive as a post-training milestone. | Small compared with current flagship open-weight families. | Open-weight |
| Llama 2 | Meta | 2023-07-18 | Meta’s family that accelerated mainstream adoption of open-weight LLMs. | Major ecosystem impact and broad downstream adaptation. | Now clearly behind newer open-weight families. | Open-weight |
| BLOOM | Hugging Face / BigScience | 2022-07-12 | A major multilingual open model built through the BigScience collaboration and distributed through Hugging Face. | Important multilingual history and open collaboration milestone. | No longer competitive with newer open-weight families on raw quality. | Open-weight |
Once model choice turns into production spend, you need visibility by provider, model, and team, not just a vendor pricing page.