Best Open Source LLM for Coding: Benchmark & Comparison [2026]

Open models can now handle far more than autocomplete and isolated code generation. The strongest releases can inspect repositories, use terminal tools, run tests, debug failures and work through multi-step engineering tasks with limited supervision.

That makes choosing the best open source LLM for coding more difficult. A model can perform well on algorithmic problems but struggle to modify a real repository. Another can achieve a strong software-engineering score only when given a large reasoning budget, a particular agent harness and generous time limits.

This comparison reviews public benchmark results, official model documentation, licensing and deployment characteristics for four models available through Aurora Inference: DeepSeek V4 Flash, DeepSeek V4 Pro, Kimi K3 and GLM 5.2.

Aurora did not run the benchmark tests cited below. The results come from the model developers and public evaluation frameworks, so they should be treated as directional evidence for shortlisting rather than a substitute for testing on your own codebase.  As we've noted in previous posts - there is no substitute for results measured on your own existing workloads.

Best Open Source LLMs for Coding in 2026

While there is no universal winner across every coding task as of this publication date, each model does have a clearer/clearest fit based on the public results reviewed for this article:

• Best for complex agentic coding: Kimi K3
• Best DeepSeek model for difficult coding tasks: DeepSeek V4 Pro
• Best for efficiency and high-volume use: DeepSeek V4 Flash
• Best permissively licensed alternative: GLM 5.2

These recommendations reflect public data available in September 2026. They are starting points, not permanent rankings. Model updates, inference providers and agent scaffolds can materially change the result.

How We Compared the Models

This article is a research-backed comparison rather than an original Aurora benchmark. We reviewed:

• official model cards and technical reports;
• repo-level software-engineering benchmarks;
• agentic and terminal evaluations;
• code-generation and reasoning results;
• context length and reasoning modes;
• model licenses and local deployment options; and
• efficiency characteristics relevant to production inference.

We did not combine scores from different sources into one numerical rating. Benchmark results are sensitive to the harness, prompt, reasoning budget, context limit, tool access, timeout and number of attempts. Two scores with the same benchmark name may still have been produced under different conditions.

For example, Z.ai reports two Terminal-Bench 2.1 results for GLM 5.2: 81.0 with the Terminus-2 harness and 82.7 with the best reported harness. The model is unchanged, but the surrounding system is different. That is why the tables below preserve the source and comparison group for each result.

For a broader framework covering quality, latency, reliability, cost and operational fit, see How to Evaluate Open-Source Models for Production Inference.

Coding Benchmarks: Which Models Perform Best?

“Coding” now covers at least three distinct capabilities:

1. generating or reasoning about isolated code;
2. understanding and modifying an existing repository; and
3. operating as an agent that can use tools, inspect failures and continue working.

The most useful model depends on which of these resembles your production workload.

Repo-Level Software Engineering

Repo-level benchmarks are more representative of day-to-day engineering than short code-generation tests. They ask a model to understand existing files, locate the source of a problem, make a change and satisfy tests or another task-specific verifier.

Kimi K3 has the strongest overall public profile in this category among the models reviewed. Moonshot AI reports 67.5 on DeepSWE, 77.8 on ProgramBench and 81.2 on FrontierSWE. In the same evaluation table, GLM 5.2 scores 46.2, 63.7 and 67.3 respectively.

Source: Kimi K3 official model card.

GLM 5.2 still produces credible repo-level results. Z.ai reports 62.1 on SWE-bench Pro and 48.9 on NL2Repo. In Z.ai's own comparison, those results exceed the listed DeepSeek V4 Pro scores of 55.4 and 35.5. The difference between that table and results reported by other developers is a reminder that benchmark configuration matters.

DeepSeek's own comparison gives V4 Pro a narrower advantage over V4 Flash on maximum reasoning effort. Pro resolves 80.6% of SWE-bench Verified tasks versus 79.0% for Flash, and scores 55.4 versus 52.6 on SWE-bench Pro. Flash remains close despite having 284 billion total parameters and 13 billion activated parameters, compared with Pro's 1.6 trillion total and 49 billion activated parameters.

Source: DeepSeek V4 official model card.

The practical reading is straightforward. Choose Kimi K3 when extended repository work is the priority. Consider V4 Pro when you need strong repo-level ability combined with broader reasoning. Test GLM 5.2 when permissive licensing and long context are important. Use V4 Flash when a small performance difference may be worth a substantial efficiency gain.

Agentic Coding and Terminal Tasks

Agentic coding models do more than return a block of code. They inspect files, invoke tools, execute commands, read test output and revise their approach. Terminal-Bench is useful here because it evaluates work performed in a terminal environment rather than code generation alone.

Moonshot AI reports 88.3 for Kimi K3 on Terminal-Bench 2.1, compared with 82.7 for GLM 5.2 in the same table. Kimi also leads GLM 5.2 on SWE-Marathon, 42.0 to 13.0, which points to a larger advantage on extended tasks.

DeepSeek's internal comparison shows a smaller but consistent Pro advantage. On Terminal-Bench 2.0, V4 Pro Max scores 67.9 while V4 Flash Max scores 56.9. Pro also leads Flash on Toolathlon and difficult tool-assisted browsing evaluations.

Reasoning settings have a large effect. DeepSeek V4 Flash moves from 49.1 in non-thinking mode to 56.9 at maximum effort on Terminal-Bench 2.0. V4 Pro rises from 59.1 to 67.9. Higher reasoning effort can improve task completion, but it also increases generated tokens and total response time.

This is why an agentic coding benchmark should always disclose the full setup. The model name alone does not describe the system being tested.

Code Generation and Reasoning

For isolated code generation, DeepSeek V4 Pro is the strongest of the two DeepSeek models. DeepSeek reports 93.5 on LiveCodeBench at maximum effort, compared with 91.6 for V4 Flash. Its reported Codeforces rating is also higher, at 3,206 versus 3,052.

Kimi K3 performs strongly on broader scientific and program reasoning. Moonshot reports 58.7 on SciCode and 77.8 on ProgramBench. GLM 5.2 records 50.5 and 63.7 in the same table.

These results are useful, but algorithmic performance should not dominate a production decision. A model that writes a correct standalone function may still struggle to locate the right files, preserve repository conventions or recover from a failing test. For most software teams, repo-level and agentic evaluations deserve more weight than competitive-programming scores.

Speed and Latency for Coding Workloads

Coding quality is only part of the developer experience. A model that produces a slightly better answer but takes several minutes to respond may be a poor choice for interactive use. A faster model may be more valuable for autocomplete, rapid iteration or workflows that make many sequential calls.

Three measurements matter:

• Time to first token: how quickly the response begins;
• Output speed: how many tokens the deployment generates per second; and
• Time to successful completion: how long it takes to produce a result that passes the task.

The third metric is the most useful. A fast but unreliable generation can lose its advantage if the developer or agent must retry it several times.

Speed also depends heavily on the inference provider, hardware, quantization, batch size and reasoning configuration. Model-level benchmark claims cannot tell you exactly how an endpoint will perform in production.

Architecture still gives useful directional guidance. DeepSeek V4 Flash activates 13 billion parameters per token, compared with 49 billion for V4 Pro. DeepSeek positions Flash as the smaller, more efficient model, while Pro is designed for stronger performance on difficult knowledge and agentic tasks. Kimi K3 activates 104 billion of its 2.8 trillion parameters, making it the heaviest model in this comparison. GLM 5.2 is a 753-billion-parameter mixture-of-experts model with approximately 40 billion active parameters.

For latency-sensitive coding assistance, V4 Flash is the clearest model to test first. For long-running agents, the better metric is completed work per hour or cost per successful task, where a slower but more capable model may recover the difference.

What Does Each Coding Model Cost?

Token pricing changes by provider, service tier, caching policy and model version. Self-hosted costs also depend on hardware, utilization and engineering overhead. A static API price table can become outdated quickly and may not match the rates available through Aurora Inference.

The more durable comparison is relative:

• DeepSeek V4 Flash is designed as the lower-cost, high-throughput member of the V4 family.
• DeepSeek V4 Pro uses a larger active model and targets harder reasoning and agentic tasks.
• Kimi K3 has the largest active parameter count in this group, which raises serving requirements despite its sparse architecture and native quantization.
• GLM 5.2 sits between the smaller Flash model and Kimi K3 in active parameter count, with architecture changes intended to reduce the cost of million-token context.

Do not compare models on input and output rates alone. Coding agents may make many calls, generate long reasoning traces and retry failed approaches. The relevant metric is often the cost of reaching a correct, tested result.

For a full framework and worked examples, see Too Many Open Models? Stop Trying to Pick the Perfect One.

Open Source vs Open Weight: Does Licensing Matter?

Worth noting: “Open source LLM” is the common search term, but open model releases do not all provide the same rights.

DeepSeek V4 Flash, DeepSeek V4 Pro and GLM 5.2 are released under the MIT License. This is a permissive license that generally supports commercial use, modification and redistribution, subject to its terms.

Kimi K3 is released under the separate Kimi K3 License. Its weights are available for deployment and further development, but teams should review the license itself before deciding that it meets a particular commercial or redistribution requirement.

Licensing matters most when you plan to:

• host the model on your own infrastructure;
• modify or fine-tune its weights;
• redistribute a derivative model;
• embed the model in a commercial product; or
• operate in a market with additional contractual or regulatory requirements.

If you only access a hosted endpoint, the provider's service terms and data policies also matter. Open weights do not automatically determine how prompts are retained, where inference runs or what reliability commitments apply.

Which Open Source LLM Should You Choose for Coding?

• Choose Kimi K3 for complex, long-horizon engineering work when task completion matters more than raw speed. It has the strongest agentic coding profile in the latest public results reviewed here, including leading results against GLM 5.2 on DeepSWE, Terminal-Bench 2.1, FrontierSWE and SWE-Marathon.
• Choose DeepSeek V4 Pro when you want a strong general coding and reasoning model with an MIT license. It consistently improves on V4 Flash across DeepSeek's hardest coding and agentic evaluations, although the gap is smaller on some repo-level tasks.
• Choose DeepSeek V4 Flash for high-volume coding support, interactive workflows and cases where efficiency matters. Its benchmark results remain close to V4 Pro on LiveCodeBench and SWE-bench despite its smaller active footprint.
• Choose GLM 5.2 when permissive licensing, a one-million-token context window and flexible local deployment are priorities. It is competitive on several repo-level evaluations, though its position changes depending on the harness and source.

The final decision should come from a controlled evaluation on your own workload. Use the same prompts, repository state, tools, reasoning settings, time limits and success criteria for every model. Record latency, token usage, retries and task completion rather than judging a model from one impressive output. For a systematic comparison process, see our full model evaluation framework.

DeepSeek V4 Flash, DeepSeek V4 Pro, Kimi K3 and GLM 5.2 are available through Aurora Inference, giving teams one place to compare leading open models and select the right model for each coding workload.

Explore the models on Aurora Inference

Frequently Asked Questions

Can I run these models locally?

Yes, if they have permissive licenses. DeepSeek V4 Flash, V4 Pro, and GLM 5.2 are all released under the MIT License, which allows local deployment and modification. Kimi K3 is released under its own custom license—review the terms before self-hosting. You'll need sufficient GPU memory and inference infrastructure; model size ranges from 13 billion (V4 Flash active) to 104 billion (Kimi K3 active) parameters.

What's the difference between open source and open weight?

Open source typically means the model weights, code, and documentation are all available under a permissive license. Open weight means weights are available but may come with restrictions on use or redistribution. The distinction matters most if you plan to modify, fine-tune, or commercialize a model. For hosted access via an endpoint, the provider's terms matter more than the model's license.

Which model is fastest for coding tasks?

DeepSeek V4 Flash is designed for low-latency inference due to its smaller active parameter count (13 billion). It produces competitive benchmark results on code generation and repo-level tasks despite being the smallest model in this comparison. Speed also depends heavily on your inference provider, hardware, and reasoning settings—benchmark claims do not guarantee production performance on your endpoint.

How do I choose between these models for my workload?

Test on your own codebase. Use the same prompts, repository state, tools, reasoning settings, time limits, and success criteria for each model. Record latency, token usage, retries, and task completion rates. This article summarizes public benchmarks as a shortlisting tool, not a substitute for hands-on evaluation.

Can DeepSeek V4 Pro do everything V4 Flash can do?

Pro performs better on harder reasoning and agentic tasks, but not uniformly better on every benchmark. On some repo-level evaluations, Flash is close. Pro's advantage grows on extended reasoning and tool-assisted tasks. If efficiency matters more than marginal performance gains, Flash is often the better choice.

What does "terminal-bench" measure?

Terminal-Bench evaluates models operating as agents in a terminal environment. Rather than returning code blocks, the model inspects files, invokes tools, reads output, and revises its approach. This is closer to real-world coding agent workloads than isolated code-generation tests.

Do I need to worry about model licensing if I only use hosted endpoints?

Not primarily. If you access a model through a hosted API (like Aurora Inference), the provider's service terms and data policies matter more than the model's open-source license. Licensing becomes important when you self-host, fine-tune, or redistribute the model itself.

How often do these benchmarks change?

Public benchmarks are updated regularly as models improve and new evaluation harnesses emerge. The results in this article reflect data available in September 2026. Model updates, new inference providers, and agent scaffolds can materially change performance rankings. Re-evaluate periodically if model choice is central to your architecture.  Our team will also be publishing some primary research on this topic in the coming weeks, so stay tuned to our blog for updates on this topic.

Sources

• DeepSeek V4 official model card and evaluation results
• Kimi K3 official model card and evaluation results
• GLM 5.2 official model card and evaluation results
• GLM 5.2 release article