GLM-5.3-Flash vs DeepSeek V4 Flash vs Qwen: Which for Coding?
In this article
Starting from scratch, I would try GLM-5.3-Flash as the everyday coding default. It has the lowest recorded token quote in this group and competitive code-generation and completion results. For an agent that edits files and runs commands, put Qwen3.8-Flash-Next in the first trial. If DeepSeek V4 Flash 0731 already works reliably in your project, compare real tasks before replacing it.
That is a trial order. Here is the evidence behind it, and what the price difference actually buys.
Three models, with the versions pinned down
This article uses the September 7, 2026 benchmark snapshot. DeepSeek means the 0731 release. Qwen means the Flash-Next entry in Artificial Analysis and LiveBench, labelled Qwen3.8-Next-Flash on ModelRank. It is separate from Qwen3.8 Max 0902.
| Measure | GLM-5.3-Flash | DeepSeek V4 Flash 0731 | Qwen3.8-Flash-Next |
|---|---|---|---|
| ModelRank editorial tier | A | B+ | A |
| Coding Index, DeepSeek = 100 | 105 | 100 | 105 |
| AA Terminal-Bench 2.1 | 84.3% | 78.7% | 86.1% |
| AA SciCode | 51.6% | 50.3% | 50.6% |
| LiveBench Coding | 78.95 | 74.98 | 72.55 |
| LiveBench Agentic Coding | 56.77 | 46.77 | 61.62 |
The useful disagreement is in the last two rows. GLM leads this group on ordinary Coding; Qwen leads on Agentic Coding. A developer asking for a function and a developer delegating a repository change have good reasons to try different defaults.
ModelRank’s index combines just the two AA tests, using equal-weight geometric means of model-to-baseline ratios. An index of 105 does not mean 5% more real-world development capability. LiveBench Coding averages generation and completion; Agentic Coding averages JavaScript, TypeScript and Python. Those are supplementary evidence, not extra terms in the index.
AA records max effort for GLM and DeepSeek; Qwen’s effort is unspecified. Harnesses, budgets and samples may differ across sources. These numbers narrow a shortlist; they do not settle a controlled comparison in your repository.
Put the quotes into a workload
For 1 million input tokens plus 200,000 output tokens, token charges would be:
| Model and channel | USD per million input / output | Example cost |
|---|---|---|
| GLM, recorded OpenRouter quote | $0.075 / $0.25 | $0.125 |
| Qwen, corresponding recorded OpenRouter entry | $0.15 / $0.47 | $0.244 |
| DeepSeek official, off-peak | $0.22 / $0.66 | $0.352 |
| DeepSeek official, peak | $0.44 / $1.32 | $0.704 |
The OpenRouter quotes were saved September 6. DeepSeek’s official table was checked again September 8. These are explicitly different purchasing channels, not a comparison of three providers’ standard API prices. Check the actual model ID, routing and current quote before buying.
DeepSeek peak hours are Monday–Friday, UTC 01:00–04:00 and 06:00–10:00; all other hours are off-peak. Its input figures here assume cache misses. Cache hits, tool charges, additional reasoning tokens and retries can change the bill.
Give each candidate a fair first job
Small fixes, functions and test additions: GLM is a sensible first trial based on its quote and Coding results. That does not establish a win in every language or framework.
Repository work performed by an agent: Qwen’s Agentic results deserve a closer look. Watch whether it finishes the change after tests fail, not just whether its first patch looks plausible.
An established DeepSeek integration: your tool adapters, prompts and cache strategy have value. A lower token quote does not automatically pay for replacing them.
Take ten recent tasks: four small fixes, three test additions, two cross-file changes and one issue a model previously failed. Start each candidate from the same commit, with the same permissions, acceptance criteria and budget. Record the reasoning settings actually used.
For every run, keep four fields: tests passed, requirements respected, human repair time and total cost. Keep failed runs too. Ten tasks can expose a poor fit; they cannot establish a statistically reliable world ranking.
Keep the default that reduces rework. If two candidates meet your acceptance standard, cost becomes a useful deciding factor.
For a narrower decision, read GLM vs DeepSeek, GLM vs Qwen, or Qwen vs DeepSeek. The coding tier list remains the single overview.
Sources
AA GLM, AA DeepSeek, AA Qwen, LiveBench CSV, DeepSeek pricing, GLM channel, Qwen channel.
The LiveBench filename is a dataset version, not every model’s evaluation date. ModelRank’s evidence snapshot retains the measurements and methodology. The ten-task exercise above is a suggested evaluation; benchmark results come from the cited third parties.