GLM-5.2 vs DeepSeek V4 vs Qwen3: The Open-Weights Coding Model Showdown (2026)

Last updated: June 17, 2026

The interesting fight in coding models is no longer open versus closed. It is open versus open. Three labs now ship weights you can download, self-host, and route to per-token at a fraction of frontier pricing, and all three post real software-engineering benchmark numbers: Z.ai's GLM-5.2, DeepSeek V4, and Alibaba's Qwen3 line. If your question is "which open-weights model should I point my coding agent at," this is the comparison built to answer it.

This piece is part of our model-economics beat. For the single-model deep dives, see our GLM-5.2 cost math, our DeepSeek V4 economics breakdown, and the self-hosting break-even math. For the layer that decides when to use which, read model routing recipes and why the orchestration layer is the next big play.

Every figure below is attributed to a primary or named source, with verification dates. Prices and benchmarks move fast on open weights because anyone can host them - verify against the live vendor page before you commit a production budget.

A Note on Which Variant We Compare

Each of these is a family, not a single model. To keep the comparison fair, we anchor on the strongest open-weights variant from each lab, because the whole point of this category is downloadable weights:

• GLM-5.2 - Z.ai's flagship, open weights under MIT.
• DeepSeek V4 Pro - the flagship tier (we cover V4 Flash separately), open weights under MIT.
• Qwen3.6-35B-A3B - Alibaba's open-weights coding MoE under Apache 2.0. Alibaba's larger Qwen3.7-Max is the stronger model on paper but is API-only with no open weights, so it sits outside this category; we flag it where relevant.

That last point is the single most important caveat in this post: in mid-2026, Qwen's very best coding model is closed. The open-weights Qwen you can actually self-host is a much smaller MoE - which, as the numbers show, punches far above its size.

The Contenders at a Glance

Sources for this table are in the Sources section. The standout structural facts: DeepSeek V4 Pro is by far the largest (1.6T total), GLM-5.2 sits in the middle, and Qwen3.6-35B-A3B is two orders of magnitude smaller in total parameters and activates only 3B per token - which is why it is the only one of the three that runs on a single consumer-class GPU.

Benchmarks: Read the Cluster, Not the Ranking

Coding benchmarks for open-weights models are reported by a mix of vendors, aggregators, and third-party evaluators using different scaffolds. Treat them as a cluster, not a leaderboard, and never compare a SWE-bench Verified number against a SWE-bench Pro number - they are different, harder tests.

A few honest reads of this table:

• GLM-5.2 leads the SWE-bench Pro and Terminal-Bench numbers it reports, and its Artificial Analysis Intelligence Index of 51 is described as well above the median for open-weight models of similar size. Note that artificialanalysis.ai does not list a separate SWE-bench Verified figure for GLM-5.2, so we leave that cell blank rather than borrow a number from a less reliable source.
• DeepSeek V4 Pro-Max posts the strongest SWE-bench Verified figure of the three (around 80.6%, per third-party trackers), but its SWE-bench Pro and Terminal-Bench numbers come from vendor-style scaffolds and should be treated as indicative.
• Qwen3.6-35B-A3B scoring 73.4 on SWE-bench Verified with only 3B active parameters is the most surprising data point in the set. It is not the top score, but on a per-active-parameter and per-watt basis nothing else here is close.

The practical takeaway: on raw quality, DeepSeek V4 Pro and GLM-5.2 are frontier-substitute class, while Qwen3.6-35B-A3B is a remarkable efficiency play that trades a few points of capability for a dramatically smaller footprint.

Pricing: The Live Page Is the Only Source of Truth

Open-weights pricing is a moving target because the original lab is just one of many hosts. Here are the official first-party API list prices, verified June 17, 2026.

GLM-5.2 list pricing is $1.40 / $4.40, with a provider median closer to $0.55 / $1.85 because the open weights let third parties compete on hosting (Artificial Analysis). DeepSeek's live pricing page now lists V4 Pro at $0.435 / $0.87 with a near-free cache hit of $0.003625, and V4 Flash at $0.14 / $0.28 (api-docs.deepseek.com/quick_start/pricing, verified June 17, 2026).

One important correction worth flagging: a lot of April launch coverage - and our own earlier DeepSeek V4 economics post - quoted V4 Pro at $1.74 / $3.48. The live DeepSeek pricing page as of June 17, 2026 lists $0.435 / $0.87, roughly a quarter of that. The live page is the source of truth; if you are modeling spend, use it, not the launch articles.

Qwen3.6-35B-A3B itself is open weights, so its "price" depends entirely on who hosts it or your own hardware. The Qwen3.6 Plus API line above ($0.325 / $1.95 at a 35% promo, per OpenRouter) is included as a managed-API reference point for the Qwen family, not as the price of the 35B open-weights model specifically.

What a Real Task Costs

Per-token rates do not tell you what work costs. Model one agentic coding task: roughly 40,000 input tokens (repo context, files, tool results) and 8,000 output tokens (plan plus diffs), no caching.

At list prices, DeepSeek V4 Pro is the cheapest of the high-quality options on this input-heavy profile, and its near-zero cache-hit rate makes repeated-context agent loops cheaper still. GLM-5.2 closes most of the gap if you shop the open-weights hosting market for the lower provider-median rate. The caveat that survives every pricing table: cheap per token only becomes cheap per task if the model lands the work in as few attempts as a pricier model, which is exactly why the benchmark cluster above matters.

Context Windows and Output Limits

All three flagships are built for repo-scale work.

• GLM-5.2: 1M-token context, max output around 128K-131K depending on host (llm-stats.com).
• DeepSeek V4 Pro and Flash: 1M-token context, 384K max output - the largest output ceiling of the three, and a genuine differentiator for tasks that emit huge diffs or long structured plans (DeepSeek pricing docs).
• Qwen3 family: the managed Qwen3.6 Plus and Qwen3.7-Max APIs advertise 1M context; the open-weights Qwen3.6-35B-A3B supports long context but useful self-hosted context length is bounded by your VRAM (see below).

If your workload is "drop a whole repo in and ask for a coordinated change," any of the three handles the input side. DeepSeek's 384K output ceiling is the one to reach for when the model has to write a lot, not just read a lot.

Self-Host Footprint: Where Qwen3 Wins Outright

This is the category that separates "open weights in principle" from "open weights you will actually run."

• GLM-5.2 (753B / 40B active): a multi-GPU server-class deployment. Open weights are on Hugging Face under MIT, but serving a 753B MoE means real GPU capacity and ops investment. The economics only favor self-hosting at sustained high volume - see our break-even math.
• DeepSeek V4 Pro (1.6T / 49B active): the heaviest to self-host. The weights are open, but a 1.6T-parameter checkpoint is a serious infrastructure commitment; most teams will consume V4 via the (very cheap) API rather than host it. V4 Flash (284B / 13B active) is the more realistic self-host target in the DeepSeek family.
• Qwen3.6-35B-A3B (35B / 3B active): runs at roughly 21GB VRAM at Q4_K_M (about 37GB at Q8), which fits a single 24GB GPU, with reports of it running on as little as 6GB via llama.cpp at reduced speed (Will It Run AI, knightli.com). This is the only model here a solo developer can realistically self-host on a workstation.

If "no per-token bill, runs on hardware I already own" is your hard requirement, the decision is effectively made for you: Qwen3.6-35B-A3B is the open-weights coder that fits on a single GPU, and it is the only one of the three in that class.

The Decision Matrix: Pick X If...

Pick GLM-5.2 if you want the strongest reported SWE-bench Pro and Terminal-Bench numbers among true open-weights models, you are consuming it via API or a managed host, and you value an MIT license with a wide ecosystem of coding-tool integrations. It is the "best raw coding quality you can also download" pick.

Pick DeepSeek V4 Pro if unit cost is your primary axis and you want frontier-substitute quality. At $0.435 / $0.87 with a near-free cache hit and a 384K output ceiling, it is the cheapest high-quality option for input-heavy, cache-friendly agent loops. Route to V4 Flash ($0.14 / $0.28) for the bounded, high-volume inner-loop steps where you do not need Pro-level reasoning.

Pick Qwen3.6-35B-A3B if you must self-host on modest hardware, want zero per-token cost, or care about latency and data residency. Scoring 73.4 on SWE-bench Verified from a 3B-active model that fits on a 24GB GPU is the best capability-per-footprint deal in open weights right now. Choose the managed Qwen3.7-Max API only if you need Qwen's absolute top coding quality and can accept that it is closed-weights.

Route across all three if you are running real volume. The honest answer for most production setups is not "pick one" but "tier them": cheap open-weights for the easy majority, a frontier-substitute for the hard reasoning, with a failover chain. That is precisely the pattern in our model routing recipes, and the reason the orchestration layer is where the margin is moving.

Frequently Asked Questions

Which open-weights coding model is cheapest?

At first-party API list prices verified June 17, 2026, DeepSeek V4 Pro is the cheapest high-quality option at $0.435 input / $0.87 output per million tokens, with a near-free cache-hit input price of $0.003625. DeepSeek V4 Flash is cheaper still at $0.14 / $0.28 for lighter work. GLM-5.2 lists at $1.40 / $4.40 on Z.ai but has a provider median closer to $0.55 / $1.85 because third parties host the open weights.

Which open-weights model is best on coding benchmarks?

It depends on the benchmark, and you should read them as a cluster. GLM-5.2 leads the SWE-bench Pro (62.1) and Terminal-Bench 2.1 (81.0) figures it reports. DeepSeek V4 Pro-Max posts the strongest SWE-bench Verified number of the three (around 80.6%). Qwen3.6-35B-A3B scores 73.4 on SWE-bench Verified, which is exceptional for a model with only 3B active parameters.

Which one can I actually self-host on my own hardware?

Qwen3.6-35B-A3B is the only one of the three that runs on a single consumer-class GPU - about 21GB VRAM at Q4_K_M, fitting a 24GB card. GLM-5.2 (753B) and DeepSeek V4 Pro (1.6T) require multi-GPU server-class deployments, so most teams consume them via API rather than self-host.

Is Qwen's best coding model open weights?

No. As of mid-2026, Alibaba's strongest coding model, Qwen3.7-Max, is API-only on DashScope ($2.50 / $7.50 per million tokens) with no published weights. The best Qwen you can self-host is the smaller Qwen3.6-35B-A3B under Apache 2.0.

What licenses do these use?

GLM-5.2 and DeepSeek V4 are released under the MIT license; Qwen3.6-35B-A3B is under Apache 2.0. All three permit commercial use and self-hosting.

Sources

Figures verified June 17, 2026. Benchmark scores are reported by different evaluators on different scaffolds; treat them as a cluster, not an exact ranking, and re-verify against the live vendor pages before making a production decision.