Best Open-Source AI Models 2026: The Data Comparison

Open-source models, more precisely open-weights models, give you the model weights and with them control: you can self-host, customize and run them independently of any single provider. Among the open models, in the LLM Stats data snapshot from June 3, 2026, Kimi K2.6 by Moonshot AI leads the reasoning ranking (58.1), closely followed by DeepSeek-V4-Pro-Max (57.0). In the Code Arena, GLM-5.1 by Z.ai is in front, the largest context window is offered by DeepSeek-V4-Pro-Max (1.0M), and the cheapest is Gemma 4 31B by Google. No open model currently reaches the proprietary top tier such as Claude Opus 4.8 (reasoning 65.7), but the gap has narrowed.

The following figures come from the public LLM Stats leaderboard and its Open LLM view, an independent ranking with verified benchmarks, provider prices and live performance. The data snapshot is June 3, 2026.

Short Answer: Which Open-Source Model Is the Best?

For most applications, Kimi K2.6 is currently the best open all-round model, because it delivers the strongest reasoning among the open-weights models. If you need a large context window, choose DeepSeek-V4-Pro-Max; if you prioritize coding outputs by user preference, GLM-5.1; and if you want to save as much as possible, Gemma 4 31B.

Open source is not only a question of performance, but also of control. Self-hosted models keep sensitive data in-house, can be fine-tuned for your own tasks and avoid dependence on a single provider. For many teams, these advantages outweigh the remaining performance gap to the proprietary top tier.

Comparison: The Best Open-Source Models

Data View: Reasoning Score of the Open Models

Reasoning measures thinking and problem-solving ability and is the most meaningful single value for demanding tasks. Among the open models, Kimi K2.6 and DeepSeek-V4-Pro-Max are in front, followed by the GLM series from Z.ai.

Data View: Coding Score of the Open Models

For the coding score, Kimi K2.6, DeepSeek-V4-Pro-Max and GLM-5.1 sit close together. In the Code Arena, which rates real outputs by user preference, GLM-5.1 leads with an Elo of 1,754. Anyone who programs should pay attention to both signals.

Data View: Price per 1M Tokens

A big advantage of open models is the low price, especially for API usage via external providers. The values are blended prices per 1M tokens (8:1 input to output), lower is better. Gemma 4 31B is by far the cheapest, while the strongest reasoning models cost a bit more.

Best Open-Source AI by Use Case

Best Open All-Round Model: Kimi K2.6

If you're looking for a strong, versatile open model, choose Kimi K2.6 by Moonshot AI. With a reasoning score of 58.1 and 90.5% GPQA, it is the strongest open-weights model for demanding tasks. It offers a solid context window of 262K tokens and costs only $1.29 per 1M tokens via the API.

Best Open Model for Long Documents: DeepSeek-V4-Pro-Max

When the context window matters, for example for long documents, large codebases or extensive knowledge collections, DeepSeek-V4-Pro-Max is the best open choice. It is the only open model in the comparison offering 1.0M tokens of context and at the same time delivers strong reasoning with a score of 57.0.

Best Open Model for Coding: GLM-5.1

For programming tasks, GLM-5.1 by Z.ai is particularly interesting. It leads the open Code Arena with an Elo of 1,754, clearly ahead of the other open models, and matches the top on the coding score (43.0). Anyone who wants to use open models to generate or refactor code should start here.

Cheapest Open Model: Gemma 4 31B

For high volumes with moderate complexity, Gemma 4 31B by Google is the most economical choice. At around $0.17 per 1M tokens, it is by far the cheapest model in the comparison. You make compromises on reasoning (44.8), but for simple to medium tasks with high throughput the price-performance ratio is excellent.

Open Source vs. Proprietary: What Should You Keep in Mind?

Open models have caught up, but for pure peak performance they still trail. The best open reasoning model (Kimi K2.6, 58.1) remains behind Claude Opus 4.8 (65.7) and GPT-5.5 (62.3). For many tasks this gap is irrelevant; for the hardest reasoning tasks it can be decisive.

In return, open models offer advantages that proprietary ones do not have: full data control through self-hosting, the ability to fine-tune for your own tasks and independence from a provider's pricing and availability decisions. With self-hosting, however, factor in the infrastructure costs. The low API prices apply to hosted endpoints; running your own GPUs can be more or less expensive depending on utilization.

The licenses also matter. "Open source" does not automatically mean unrestricted commercial use. The range goes from permissive licenses like Apache 2.0 to custom licenses with conditions, such as restrictions on commercial use or certain application areas. Check the model's specific license before you put it into production.

How Should You Compare Open Models Fairly?

A benchmark score is a good starting point, but no substitute for your own test. Reasoning and coding scores measure average performance; whether a model fits your language, your domain and your tasks only your own eval will show. In addition to performance, consider the license, the hosting options, the context window and the actual costs in your setup.

The data snapshot here is June 3, 2026. The open-source space is evolving especially fast; new versions of Kimi, DeepSeek, GLM, Qwen and Gemma appear at short intervals. Check the current figures again before making a decision.

Conclusion

Open-source AI is a serious alternative in 2026. Kimi K2.6 is the best open all-round model, DeepSeek-V4-Pro-Max the best choice for long contexts, GLM-5.1 the coding winner and Gemma 4 31B the cheapest option. No open model yet reaches the absolute proprietary top tier, but for teams that prioritize data control, self-hosting or low costs, these models are a very good choice. Before deployment, check the license, hosting costs and suitability with your own test.

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