No single open-source coding model can be named the best for 2026 on the published evidence. The three models were scored on different benchmarks, under different agent setups, and mostly by their own developers. DeepSeek-V4-Flash-0731 is ahead of GLM-5.2 on all four benchmarks that DeepSeek publishes side by side with it. Qwen3-Coder-Next reports results on SWE-Bench Verified, a benchmark that the other two models’ published figures do not include. The practical answer is to choose by license, deployment path and workflow, then test a shortlist on your own code.
Which checkpoints are being compared
The Qwen model here is Qwen3-Coder-Next, the coding-agent model described in the Qwen authors’ March 2026 technical report (arXiv:2603.00729). Do not treat that name as a stand-in for every Qwen3-Coder build. The DeepSeek model is DeepSeek-V4-Flash-0731. DeepSeek’s model card states: “DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities.” If you have seen earlier V4-Flash preview material, the 0731 checkpoint is the one to evaluate. GLM-5.2 is published by Z.ai, whose official GLM-5 repository lists GLM-5.2 checkpoints.
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| Checkpoint | Publisher and primary source | Total parameters | Active parameters per forward pass | License as stated | Context length |
|---|---|---|---|---|---|
| Qwen3-Coder-Next | Qwen authors, technical report (March 2026) | 80 billion (Qwen report) | 3 billion (Qwen report) | Described as open-weight; the license name and text were not verified for this article | Not stated in the published material |
| GLM-5.2 | Z.ai, GLM-5 repository | Not stated in the published material | Not stated in the published material | Not established; check the license text on the checkpoint’s model card | Not stated in the published material |
| DeepSeek-V4-Flash-0731 | DeepSeek, model card; parameter counts from the V4 preview announcement | 284 billion (V4 preview announcement, for V4-Flash) | 13 billion (V4 preview announcement, for V4-Flash) | MIT License for repository and weights (model card) | Million-token context, a DeepSeek claim in the V4 preview announcement |
These parameter counts are publisher-reported architecture figures. Total parameters set how much weight data a deployment has to hold. Active parameters describe how much of the network runs for each token, so they bear on compute more than on storage. Neither figure is a measured speed or memory requirement, and the published material gives no memory or throughput figures for any of the three models.
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Only one table puts GLM-5.2 and DeepSeek-V4-Flash-0731 side by side, and it is DeepSeek’s own model card. Qwen’s scores come from a separate report that uses different benchmarks and scaffolds. The published figures do not include one benchmark that all three models were scored on, so they cannot be combined into a single ranking.
#1 Best Overall
DeepSeek’s table: DeepSeek-V4-Flash-0731 against GLM-5.2
| Benchmark | DeepSeek-V4-Flash-0731 | GLM-5.2 | What the figure is |
|---|---|---|---|
| Terminal Bench 2.1 | 82.7 | 81.0 | Terminal-agent tasks; vendor model-card figure |
| NL2Repo | 54.2 | 48.9 | Repository-level coding; vendor model-card figure |
| DeepSWE | 54.4 | 46.2 | Coding-agent benchmark; vendor model-card figure |
| DSBench-FullStack | 68.7 | 61.8 | Internal test set according to DeepSeek’s model card, so outside parties cannot run it from the card |
Every figure in this table is vendor-reported, and the published material does not establish an independent reproduction of any of them. DeepSeek specifies its agent framework and reasoning-effort setting for its public coding-agent evaluations. The largest gap is on DeepSWE, at 8.2 points, and the smallest is on Terminal Bench 2.1, at 1.7 points. A gap that small, from one vendor’s run, does not show that either model is better at terminal work.
Qwen’s results: SWE-Bench Verified under three scaffolds
Qwen reports SWE-Bench Verified scores of 70.6% with SWE-Agent, 71.1% with MiniSWE-Agent and 71.3% with OpenHands. These are the Qwen authors’ results, each tied to the scaffold named beside it. The three scores differ only slightly, but they still differ by scaffold, so any figure should be quoted with its scaffold. The Qwen report also covers Terminal-Bench 2.0, function-level code, competitive programming, full-stack and text-to-SQL tasks. Those figures are not reproduced here.
Why the numbers cannot be merged
- Benchmark versions differ. Qwen reports Terminal-Bench 2.0, while DeepSeek’s table reports Terminal Bench 2.1. Those are not one scale.
- Benchmarks differ. SWE-Bench Verified appears only in the Qwen report. NL2Repo, DeepSWE and DSBench-FullStack appear only in DeepSeek’s table.
- Setups differ. DeepSeek’s public coding-agent runs use DeepSeek Harness in minimal mode, maximum reasoning effort, temperature 1.0 and top_p 0.95. Qwen reports under named scaffolds. The run conditions behind GLM-5.2’s figures are not separately documented in the published material.
- Some sets are private. DeepSeek labels DSBench-FullStack and DSBench-Hard as internal test sets.
How to choose, by constraint
- You need explicit MIT terms on weights and code, plus a documented local serving path. DeepSeek-V4-Flash-0731 is the only one of the three whose license terms appear in the published material. Its model card includes local serving instructions, and DeepSeek documents an API for hosted use.
- Your main work is repository repair or terminal-agent tasks. Put DeepSeek-V4-Flash-0731 and GLM-5.2 through the same test run first. They are the only two whose published figures can be compared directly, and DeepSeek-V4-Flash-0731 is higher on all four benchmarks in its own table.
- You want a small active-parameter model for local coding agents. Qwen3-Coder-Next lists 3 billion active parameters, the smallest active count among the figures published here. That is a reason to test it locally, not a measured speed or memory advantage. GLM-5.2’s parameter counts are not established in the published material.
- Your work is code generation, competitive programming or text-to-SQL. The Qwen report covers these tasks. The published material gives no equivalent figures for GLM-5.2 or DeepSeek-V4-Flash-0731, so you will need to measure them yourself.
- You need a hosted endpoint with predictable cost. DeepSeek documents its API. The published material does not establish a hosted price or latency comparison across the three models, so compare current pricing and measured latency on your own prompts.
Running each model locally or hosted
DeepSeek-V4-Flash-0731
The model card includes local serving instructions, and the repository and weights are under the MIT License. With 284 billion total parameters, the weights are large, so plan storage and memory around the full checkpoint rather than the 13 billion active figure. Copy serving commands from the model card itself; this article does not reproduce them.
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The Qwen report describes the model as open-weight and specialized for coding agents and local development. The published material does not give checkpoint formats, serving commands or hardware requirements, so confirm them on the release page for the exact build you plan to run.
GLM-5.2
The Z.ai GLM-5 repository lists GLM-5.2 checkpoints in BF16 and FP8 precision, along with links to deployment frameworks. An FP8 checkpoint stores each weight in 8 bits rather than 16, so it takes less space, but the published material gives no memory figures for either precision.
Before you download any checkpoint
- Match the exact checkpoint name to the one in your evaluation.
- Read the license file on that checkpoint’s own page rather than a summary.
- Confirm that the precision you need is published for that build.
- Size hardware from the checkpoint’s actual files and your serving framework, then measure throughput on your own prompts.
Hosted use
DeepSeek documents its API, and the model card points to inference providers. The published material does not say which providers host GLM-5.2 or Qwen3-Coder-Next, and it gives no hosted price or latency comparison for any of the three.
Rank #4
Licensing at a glance
| Model | License stated in the published material | What to verify |
|---|---|---|
| Qwen3-Coder-Next | Described as open-weight; the license name and text are not established here | The license file on the exact Qwen3-Coder-Next checkpoint |
| GLM-5.2 | Not established here | The license text on the Z.ai GLM-5 repository and the checkpoint’s model card |
| DeepSeek-V4-Flash-0731 | MIT License for the repository and weights, per the model card | That the model card still shows the same terms on the day you download |
Testing the shortlist on your own code
Because the published scores share no benchmark and no setup, a short test on your own repositories is the most reliable comparison for your work. Keep these fixed across all models:
- The same agent scaffold and tool set for every model.
- The same reasoning-effort, temperature and top_p settings. DeepSeek’s published runs used maximum reasoning effort, temperature 1.0 and top_p 0.95, which is a reasonable starting point if you record each model’s recommended settings alongside them.
- Pinned checkpoint names, so a later release does not change results mid-test.
- Tasks from your own backlog: real bug fixes with tests, a few terminal workflows, and one code-generation task each.
Record the pass rate, the number of retries and the wall-clock time for each task. Those three numbers answer the question that the published benchmarks cannot.
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