Key Takeaways
- Kimi K2.6, released April 20, 2026, is a 1 trillion parameter open-weight model that ties GPT-5.5 on SWE-Bench Pro at 58.6 percent and scores 80.2 percent on SWE-Bench Verified.
- DeepSeek V4 released April 24, 2026 in two sizes, V4-Pro (1.6T parameters) and V4-Flash, both with a 1 million token context window, and V4-Pro-Max scores 80.6 percent on SWE-bench Verified.
- GLM-5, released February 11, 2026, scores 77.8 percent on SWE-bench Verified and includes a native Agent Mode that turns prompts into ready to use office documents.
- All three ship open weights: Kimi K2.6 and GLM-5 under permissive licenses, with DeepSeek V4 on Hugging Face, so you can self host any of them.
- Kimi K2.6 costs about $0.60 input and $2.50 output per million tokens on its official API, and its Agent Swarm runs up to 300 parallel sub-agents.
- DeepSeek V4’s 1 million token context is the largest here, ideal for whole repository work, and it is roughly tied with Gemini 3.1 Pro among open weights on coding.
- GLM-5 costs $1.00 input and $3.20 output per million tokens and leads on agentic document generation, with GLM-5.1 narrowing the gap to Claude Opus on coding.
- Pick Kimi K2.6 for agentic coding value, DeepSeek V4 for huge context and repository scale work, and GLM-5 for agentic workflows that produce documents.
Open source coding models caught up to the closed leaders in 2026, and three of them now sit at the front: Kimi K2.6 from Moonshot AI, DeepSeek V4, and GLM-5 from Z.ai. All three post SWE-bench scores within a few points of the best closed models, all three ship open weights you can self host, and all three are dramatically cheaper than Claude or GPT. The differences are in context size, agentic features, and cost.
This comparison breaks down how each model performs on real coding benchmarks, how big a context they handle, what agentic tooling they include, and what they cost to run. The goal is to help engineering teams pick the right open model for their workflow rather than crown a single winner, because the best choice depends on whether you prioritize cost, context, or agent features.
All three launched or updated within the last four months, so benchmark tables from 2025 are obsolete. Here is the current picture.
Quick Comparison: Kimi K2.6 vs DeepSeek V4 vs GLM 5
| Factor | Kimi K2.6 | DeepSeek V4 | GLM-5 |
|---|---|---|---|
| Best for | Agentic coding value | Huge context, repo scale | Agentic document workflows |
| SWE-bench Verified | 80.2 percent | 80.6 percent (Pro-Max) | 77.8 percent |
| Context window | 256K to 262K tokens | 1 million tokens | 200K tokens |
| Parameters | 1T total, 32B active | 1.6T total, 49B active (Pro) | 744B total, 40B active |
| API input price | About $0.60 per million | Low, repo scale friendly | $1.00 per million |
| License | Modified MIT, open weights | Open weights on Hugging Face | MIT, open weights |
What is Kimi K2.6?
Kimi K2.6 is Moonshot AI’s open-weight flagship, released April 20, 2026. It is a 1 trillion parameter Mixture-of-Experts model with 32 billion parameters active per token, so inference runs at roughly the cost of a 32B model while the full trillion parameter capacity is available for routing. Per benchmark reviews, it ties GPT-5.5 on SWE-Bench Pro at 58.6 percent and scores 80.2 percent on SWE-Bench Verified.
The headline feature for coders is Agent Swarm. It scales horizontally to 300 parallel sub-agents executing 4,000 coordinated steps at once, triple the capacity of K2.5, and can run continuously for up to 12 hours on a single task. That lets it decompose a large project into parallel, domain specialized subtasks and deliver end to end output including code, documents, and sites in one autonomous run.
It has a 256K to 262K token context window, native multimodal support, and open weights on Hugging Face under a Modified MIT license. Pricing is aggressive at roughly $0.60 input and $2.50 output per million tokens on the official API, which the reviews note is around 80 percent cheaper than comparable closed models. It is also free to use on kimi.com and the Kimi mobile app.
What is DeepSeek V4?
DeepSeek V4 released April 24, 2026 as a preview, in two variants: V4-Pro with 1.6 trillion parameters and 49 billion active, and the lighter V4-Flash with 284 billion parameters. Per DeepSeek’s specs, both models support a 1 million token context window by default, the largest of the three by a wide margin.
That context is the defining feature. DeepSeek V4 uses a hybrid attention design combining Compressed Sparse Attention and Heavily Compressed Attention to make long context efficient, requiring only about 27 percent of the inference FLOPs and 10 percent of the KV cache of V3.2 at 1 million tokens. For teams that want a model to read an entire repository at once, this is the standout.
On coding, DeepSeek-V4-Pro-Max scores 80.6 percent on SWE-bench Verified, the highest open-weights entry, effectively tied with Gemini 3.1 Pro. Both models support JSON output, tool calls, and thinking plus non-thinking modes, expose up to 384K output tokens, and ship open weights on Hugging Face. For repository scale agentic work, DeepSeek V4 is the most capable option here.
What is GLM-5?
GLM-5 from Z.ai, formerly Zhipu AI, released February 11, 2026 as a 744 billion parameter MoE model with 40 billion active, trained on Huawei Ascend chips. Per launch coverage, it scores 77.8 percent on SWE-bench Verified, leading open source models on coding and agentic benchmarks earlier in 2026 and landing within three points of Claude Opus 4.6.
GLM-5’s engineering centers on agentic intelligence. Its native Agent Mode breaks high level objectives into subtasks and can transform raw prompts or source material directly into professional office documents, producing ready to use .docx, .pdf, and .xlsx files. That makes it especially useful for workflows where code and deliverables sit side by side.
It has a 200K token context window using sparse attention, native support for Chinese, English, and 15 plus other languages, and an MIT license for free commercial use of the weights. API pricing is $1.00 input and $3.20 output per million tokens. The later GLM-5.1, released March 27, 2026, narrowed the coding gap with Claude Opus to about 2.6 points and topped SWE-Bench Pro at 58.4.
Kimi K2.6 vs DeepSeek V4 vs GLM 5: Feature Breakdown
Coding Benchmarks
All three are remarkably close. DeepSeek-V4-Pro-Max edges the group on SWE-bench Verified at 80.6 percent, with Kimi K2.6 right behind at 80.2 percent and GLM-5 at 77.8 percent. On SWE-Bench Pro, Kimi K2.6 ties GPT-5.5 at 58.6 percent and GLM-5.1 tops the leaderboard at 58.4. In practice, the gaps are small enough that workflow features and cost matter more than the raw scores for most teams.
Context Window
DeepSeek V4 wins decisively with 1 million tokens, enough to load an entire codebase in one pass. Kimi K2.6 offers a strong 256K to 262K, and GLM-5 provides 200K, roughly 300 pages. If your work involves large repositories or long documents, DeepSeek V4 is the clear pick; for typical file and module level tasks, all three are more than sufficient.
Agentic Features
This is where the models differentiate. Kimi K2.6’s Agent Swarm runs up to 300 parallel sub-agents for long, complex autonomous projects. GLM-5’s Agent Mode is tuned for turning work into finished office documents. DeepSeek V4 offers solid tool calling and thinking modes at huge context. For massive parallel automation, Kimi leads; for document producing workflows, GLM-5 leads.
Cost and Self Hosting
Kimi K2.6 is the cheapest headline API price at about $0.60 input and $2.50 output per million tokens, and it is free on kimi.com. GLM-5 is $1.00 input and $3.20 output. DeepSeek V4 is priced for repository scale use and is efficient at long context. All three ship open weights, so teams with their own GPUs can self host any of them, which removes per token costs entirely for high volume use.
Which One Should You Use?
Choose Kimi K2.6 if you want the best value for agentic coding and long running autonomous tasks. Its Agent Swarm and low price make it ideal for teams that want to automate large multi step projects affordably.
Choose DeepSeek V4 if your work needs the biggest context. Its 1 million token window and top SWE-bench score make it the strongest pick for whole repository analysis, large refactors, and agentic tasks that span many files.
Choose GLM-5 if your workflow produces documents alongside code or you work heavily in Chinese and English. Its Agent Mode that outputs .docx, .pdf, and .xlsx files is a genuine differentiator for mixed coding and office work.
Verdict
The real story is that open source coding caught the closed leaders in 2026, and any of these three will serve a serious team well. DeepSeek V4 is the best for context and raw coding scale. Kimi K2.6 is the best value and the strongest for large parallel agent work. GLM-5 is the best for agentic workflows that produce finished documents. Because all three ship open weights, the smartest teams test all of them on their own codebase and self host the one that fits, since the benchmark differences are smaller than the workflow differences.
Frequently Asked Questions
Which open source coding model is the best?
It is extremely close. DeepSeek-V4-Pro-Max leads SWE-bench Verified at 80.6 percent, Kimi K2.6 follows at 80.2 percent, and GLM-5 sits at 77.8 percent. For most teams the workflow features and cost matter more than these small gaps, so the best model depends on whether you prioritize context, agent features, or price.
Which model has the largest context window?
DeepSeek V4 has the largest context window at 1 million tokens, far ahead of Kimi K2.6 at 256K to 262K and GLM-5 at 200K. That makes DeepSeek V4 the best choice for loading and reasoning over an entire repository in a single pass.
How much do these models cost?
Kimi K2.6 is roughly $0.60 input and $2.50 output per million tokens and free on kimi.com. GLM-5 is $1.00 input and $3.20 output per million tokens. DeepSeek V4 is priced for repository scale use. Because all three ship open weights, self hosting on your own hardware eliminates per token costs for high volume use.
Can I self host these models?
Yes. Kimi K2.6 and GLM-5 are released under permissive licenses with open weights, and DeepSeek V4 weights are on Hugging Face. Teams with sufficient GPU capacity can run any of them locally, which is attractive for privacy, control, and cost at scale.
Which model is best for agentic coding?
Kimi K2.6 is the strongest for large scale agentic coding thanks to its Agent Swarm, which runs up to 300 parallel sub-agents for up to 12 hours on a single task. GLM-5 is best when the agentic workflow needs to output finished documents, and DeepSeek V4 excels at agentic tasks that span huge context.
How do these compare to Claude and GPT?
They are close. Kimi K2.6 ties GPT-5.5 on SWE-Bench Pro, and GLM-5.1 narrowed the gap with Claude Opus 4.6 to about 2.6 points on coding. The open models are typically far cheaper per token, which is their main advantage over the closed leaders for high volume coding work.
Which model is best for non English coding work?
GLM-5 has the strongest multilingual support, natively handling Chinese, English, and 15 plus other languages, and matching or exceeding GPT-4 on Chinese language understanding in independent tests. For teams working across Chinese and English, GLM-5 is the natural pick.
Are these models good for whole repository refactoring?
DeepSeek V4 is the best for whole repository work because its 1 million token context can hold an entire codebase at once, and its efficient long context attention keeps inference practical. Kimi K2.6 and GLM-5 handle large files and modules well but cannot match DeepSeek’s repository scale context.
Final Recommendation
For open source AI coding in 2026, match the model to your workflow. Choose Kimi K2.6 for affordable, large scale agentic coding, DeepSeek V4 for the biggest context and repository scale tasks, and GLM-5 for agentic workflows that produce documents or need strong Chinese and English support. Since all three are open weight and benchmark within a few points, the best move is to test each on your own codebase and self host the winner.
