Last verified: July 14, 2026. Model versions (Qwen3.6, DeepSeek V4, GLM-5.2), prices, and licenses re-checked on official channels the day this went live.
Qwen vs DeepSeek vs GLM is the exact three-way most developers type when they want a Chinese open-weight model that trades blows with Claude and GPT. We ran one of them, GLM-5.2 (model string GLM-5.2[1m]), as our main coding model for three weeks at $72/mo. DeepSeek V4 posts a reported ~80.6% on SWE-bench Verified. Qwen3.6 ships under Apache 2.0, the most permissive license here. Three labs, three very different bets. Here's how they actually stack up, with a spec table, a benchmark table that flags who reported each number, and a real production test.
For agentic coding and long-horizon agents, GLM-5.2 is our pick (we ran it three weeks in production). For the cheapest tokens and RAG at scale, DeepSeek V4 Flash wins on price. For local self-hosting and the most permissive license, Qwen3 (Apache 2.0) has the widest, lightest footprint.
Quick answer:
- Qwen, DeepSeek and GLM are three separate companies (Alibaba, DeepSeek, Zhipu/Z.ai), not one model.
- Cheapest per token: DeepSeek V4 Flash ($0.14 in / $0.28 out per M; cache-hit $0.0028).
- Best hands-on coding pick: GLM-5.2 (we ran it three weeks in Claude Code); most permissive license: Qwen (Apache 2.0).
- All three now reach roughly 1M context, with per-model nuance (Qwen open models vary).
Quick disambiguation: three separate companies, not one model with three names. DeepSeek's older R1 "distill Qwen" checkpoints are a different, older thing entirely.
Qwen vs DeepSeek vs GLM at a Glance
The three families take opposite design bets. Qwen (Alibaba) is the widest lineup with the lightest self-host footprint and an Apache 2.0 license. DeepSeek (DeepSeek) is a two-tier price-performance play built on huge Mixture-of-Experts models. GLM-5.2 (Zhipu/Z.ai) is the coding and long-horizon agent specialist. Here is the spec sheet, side by side.
| Family | Vendor | Open flagship (+ closed) | Params (total / active) | Context | License | Self-host |
|---|---|---|---|---|---|---|
| Qwen | Alibaba | Qwen3.6-27B dense, Qwen3.6-35B-A3B; Qwen3-Coder-Next 80B-A3B (Max = closed/API-only) | e.g. 35B / 3B active (MoE) | up to 256K native (Coder-Next); some Plus tiers ~1M | Apache 2.0 | Lightest (27B dense ~18GB VRAM, reported) |
| DeepSeek | DeepSeek | V4 Pro (reasoning/agentic), V4 Flash (fast/cheap) | V4 Pro 1.6T / 49B; V4 Flash 284B / 13B (reported) | 1M (official) | MIT | Heavy (cluster-class MoE) |
| GLM | Zhipu / Z.ai | GLM-5.2 | 753B / ~40B active | 1M (since mid-June 2026) | MIT | Heavy |
Two notes. Qwen splits its open models from a closed, API-only "Max" flagship, so name the one you mean. And DeepSeek V4's param counts are blog-reported, not official, which is why they carry the "reported" tag.
How Do Qwen, DeepSeek and GLM Compare on Benchmarks?
No single model wins every benchmark, so read the cluster, not the ranking. On coding, GLM-5.2 leads long-horizon agent tasks while DeepSeek V4 Pro tops the aggregate coding scores. On reasoning, both push AIME near saturation. On general intelligence indexes, GLM sits mid-pack among open weights. Different harnesses make cross-model numbers apples-to-oranges, so every score below is labeled with who published it.
Legend: [SR] = self-reported by the vendor. [3P] = third-party leaderboard, independent aggregator, or our own hands-on. An n/a cell means the vendor did not publish a comparable score, not a zero.
| Benchmark | Qwen | DeepSeek V4 | GLM-5.2 | Reported by |
|---|---|---|---|---|
| SWE-bench Verified | Coder-Next 70.6-71.3% [SR]; 3.6-27B 77.2% [3P] | Pro-Max ~80.6% [3P] | n/a | Qwen report; single-blog (cautious); DeepSeek scaffold (unverified) |
| SWE-bench Pro | n/a | n/a | 62.1 [3P] | developersdigest / DataCamp (vs Claude Opus 4.8 ~69) |
| Terminal-Bench 2.1 | n/a | n/a | 81.0 [3P] | developersdigest |
| AIME 2025 | Qwen3-235B 81.5 [SR] | n/a | 99.2 [3P] | Qwen report; GLM near-saturated (ceiling effect) |
| LiveCodeBench v5 | Qwen3-235B 70.7 [SR] | n/a | n/a | Qwen report |
| BenchLM (Jul 2026) | n/a | Pro overall 87 / coding 89.8 [3P] | n/a | BenchLM Chinese-LLM ranking |
| AA Intelligence Index | n/a | n/a | 51 [3P] | Artificial Analysis |
The honest read on Chinese open-weight benchmarks in 2026: no model wins every row, and half the eye-catching numbers are self-reported. That Qwen3.6-27B 77.2% comes from a single blog, and the DeepSeek ~80.6% used an "unverified scaffold," so treat both cautiously. In our own testing we lean on the SWE-bench leaderboard and Artificial Analysis over content-farm numbers, because a scaffold change alone can move a score by a few points. When a model quotes only its own report card, discount it a notch.
DeepSeek V4: The Price-Performance King
DeepSeek V4 is the pick when every million tokens counts. It ships two tiers: V4 Pro for reasoning and agentic work, and V4 Flash for fast, cheap, high-volume calls. Its structural edge is cache pricing. If your workload re-reads the same context, like a RAG pipeline or an agent re-sending a system prompt, the cache-hit rate makes it the cheapest option here by a wide margin.
DeepSeek V4 launched as a preview on April 24, 2026. Reported architecture: a 1.6T / 49B-active MoE for V4 Pro and 284B / 13B for V4 Flash, both blog-reported rather than confirmed officially. Weights sit on Hugging Face (deepseek-ai/DeepSeek-V4-Pro, deepseek-ai/DeepSeek-V4-Flash) under MIT, with an official 1M context window.
Here is where the cache-hit economics land: V4 Flash charges $0.14 per M input on a cache miss but only $0.0028 on a cache hit, versus $0.28 output. For a RAG service hammering the same document store, that gap is the whole ballgame. Full numbers are on the official DeepSeek pricing page.
One freshness landmine nobody else flags: if you are still calling deepseek-chat or deepseek-reasoner, those endpoints retire 2026-07-24 at 15:59 UTC. Migrate to deepseek-v4-pro or deepseek-v4-flash now, because that deadline lands only ten days after this post.
Pick DeepSeek V4 for cost-sensitive, API-first products, especially anything with heavy repeated context. It is not the model you self-host on a single workstation; the big MoE wants cluster-class hardware.
Qwen3: The Widest Family and Best Self-Host Footprint
Qwen3 is the model to run on your own hardware. It is the broadest family of the three, the open models carry an Apache 2.0 license (the most permissive here), and the dense tier is light enough for a single GPU. It is also the strongest on non-coding axes like multilingual work, which the coding-only comparisons skip entirely.
The lineup is deep. On the open side you get Qwen3.6-27B (dense), Qwen3.6-35B-A3B (MoE), and the coding line topped by Qwen3-Coder-Next (80B-A3B, 256K context). Separately, Qwen3-Max is a closed, API-only flagship, so don't conflate it with the open weights. Versions and the Apache 2.0 terms are on the Qwen3-Coder GitHub repo and the Qwen team's blog.
On coding, Qwen3-Coder-Next posts 70.6-71.3% SWE-bench Verified across harnesses (self-reported, SOTA among open models with no test-time scaling). The self-host headline: the dense 27B-class reportedly runs on around 18GB of VRAM, a single 24GB card with headroom. That figure comes from one blog, so verify it on your own setup. Here's how to run these models locally, and how to serve them with vLLM or SGLang once you scale past one box.
Qwen naming is the least stable of the three, so re-confirm the current open flagship name before you pin a dependency. Pick Qwen3 for local deployment on modest hardware, multilingual coverage, or any case where you specifically need Apache 2.0 rather than MIT.
GLM-5.2: The Coding and Long-Horizon Agent Pick
GLM-5.2 is the coding and long-horizon agent specialist, and it's the one we know first-hand. It's a 753B-total / ~40B-active MoE under an MIT license, with a 1M context window since mid-June 2026 and about 131K max output. On the agentic benchmarks it holds up: SWE-bench Pro 62.1, Terminal-Bench 2.1 at 81.0, AIME near-saturated at 99.2, and an Artificial Analysis Intelligence Index of 51 (all third-party).
Here's the honest part, and it's the trust signal that separates this from a benchmark rehash: our hands-on depth is GLM only. We ran GLM-5.2 Pro as our primary coding model for three weeks across real client repos, driven through Claude Code against Z.AI's Anthropic-compatible endpoint (api.z.ai/api/anthropic, model string GLM-5.2[1m]). A normal week was about 1,300 of our ~2,000 weekly prompts. During two migration-heavy weeks we hit the weekly cap on day 5, a hard stop with no overage. It cleared a real Next.js 16 API-route refactor cleanly across roughly a dozen files. Cost: $72/mo on the GLM Coding Plan Pro tier versus the ~$200/mo we'd otherwise pay for Claude Max. For Qwen3 and DeepSeek V4 we lean on published benchmarks plus shorter API spot-checks, so weight the GLM verdict as the one we actually lived with.
The swap itself is three environment variables, which you can reuse straight from our 3 weeks running the GLM Coding Plan in Claude Code:
# Point Claude Code at GLM-5.2 via Z.AI's Anthropic-compatible endpoint
export ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic"
export ANTHROPIC_AUTH_TOKEN="your-zai-key"
export ANTHROPIC_MODEL="GLM-5.2[1m]"
claudeThree weeks on GLM-5.2 at $72/mo did the coding work we'd normally pay ~$200/mo Claude Max for, until we hit the weekly cap on day five. The full teardown lives in our full GLM-5.2 review and the coding-plan post above; this section stays short on purpose so the three-way stays equal-weight. Model details are on the Z.AI docs.
How Much Do Qwen, DeepSeek and GLM Cost?
DeepSeek V4 Flash is the cheapest per token, GLM sells a flat subscription (the Coding Plan) rather than only per-token, and Qwen's "Plus" tier sits in the middle. All three carry commercial-friendly licenses. Prices below are per 1M tokens, fetched July 14, 2026.
| Model | Input (cache miss) | Input (cache hit) | Output | Context | Notes |
|---|---|---|---|---|---|
| deepseek-v4-flash | $0.14 | $0.0028 | $0.28 | 1M | cheapest; cache-hit edge [official 2026-07-14] |
| deepseek-v4-pro | $0.435 | $0.003625 | $0.87 | 1M | reasoning/agentic [official 2026-07-14] |
| GLM-5.2 (Z.ai list) | ~$1.40 | n/a | ~$4.40 | 1M | third-party providers median ~$0.55 in / ~$1.85 out [3P] |
| Qwen3.6 Plus | ~$0.325 (35% promo) | n/a | ~$1.95 | varies | Qwen Max ~$0.80-1.25 in / ~$3.75-3.90 out [3P] |
The pricing table hides one big reframe. GLM's Coding Plan is a flat subscription, not per-token: Lite $18, Pro $72, Max $160 per month. If you code all day, that sub beats per-token GLM API spend, which is exactly why our three-week test ran on it. If you only fire occasional calls, the per-token GLM API or DeepSeek V4 Flash is cheaper.
On licensing: DeepSeek and GLM are MIT, Qwen is Apache 2.0. All three are commercial-friendly. Apache 2.0 is the most permissive if you plan to redistribute or need explicit patent terms, so confirm the exact license on the Hugging Face model card for the checkpoint you deploy.
Now the question a China-model buyer actually loses sleep over: data residency. These are Chinese providers. Can you keep data in your jurisdiction? Yes, if you self-host: open weights plus MIT or Apache 2.0 mean you can run any of them on EU (or your own region's) infrastructure and no request leaves your network. The hosted API is the opposite: your data goes to the provider, and our GLM review flags Z.ai's China-hosting and retention terms for the hosted endpoint specifically. So for strict EU hosting or compliance, self-host, and Qwen is the easiest to self-host at that. (And yes, deepseek-chat / deepseek-reasoner still retire 2026-07-24 15:59 UTC.)
Which Should You Pick?
There is no single winner, so match the model to the job. Below is the decision matrix by use case. Short version: GLM-5.2 for agentic coding, DeepSeek V4 Flash for the cheapest tokens, DeepSeek V4 Pro or GLM for the hardest reasoning, and Qwen3 for local self-host, multilingual breadth, and data residency.
| Use case | Pick | Why |
|---|---|---|
| Agentic coding / long-horizon agents | GLM-5.2 | our 3-week production test; SWE-bench Pro 62.1, Terminal-Bench 81.0 |
| Cheapest tokens / RAG at scale | DeepSeek V4 Flash | $0.14 / $0.28 per M, cache-hit $0.0028 |
| Hardest reasoning / frontier tool-use | DeepSeek V4 Pro or GLM-5.2 | BenchLM coding 89.8 vs GLM Terminal-Bench 81.0; pick by budget |
| Local self-host on modest hardware | Qwen3.6-27B dense | ~18GB VRAM (reported), Apache 2.0, lightest footprint |
| Multilingual breadth | Qwen3 | widest family, strongest non-coding axis |
| EU data residency / compliance | self-host any open model (Qwen easiest) | hosted API means data leaves your jurisdiction |
Why not Kimi? Fair question, because Kimi K2.6 (Moonshot) is real and strong: BenchLM ranks it #2 among Chinese models (overall 81, coding 88.7). We drew the line at three because "qwen vs deepseek vs glm" is the exact set people search, and a clean three-way stays useful. Kimi is the natural fourth if you want a longer shortlist, and it belongs on the wider open-source LLM leaderboard alongside Llama and Mistral. One honest paragraph, no four-way sprawl.
About the Author
Mert Batur Gurbuz is Co-Founder of Techsy.io, where the team ships AI agents, automation systems, and voice/SDR pipelines for B2B clients. He studies at the University of Birmingham and writes about the LLM tooling stack the Techsy team actually uses in production.
Co-Founder, Techsy.io — University of Birmingham. Connect on LinkedIn.
Frequently Asked Questions
Are Qwen, DeepSeek and GLM the same thing?
No. They come from three different companies: Alibaba makes Qwen, DeepSeek makes DeepSeek V4, and Zhipu/Z.ai makes GLM. The confusion usually traces to DeepSeek's older R1 "distill Qwen" checkpoints, which were DeepSeek reasoning distilled into Qwen base models. Those are an older, separate thing from today's independent flagships.
Which is best for coding in 2026?
It depends on hosted versus self-host. For hands-on agentic coding, GLM-5.2 is our pick after a three-week production test. DeepSeek V4 Pro tops the aggregate BenchLM coding score (89.8). For the strongest model you can self-host, Qwen3-Coder-Next leads open SWE-bench Verified at 70.6-71.3%. All three are genuinely usable.
Which is the cheapest per token?
DeepSeek V4 Flash, comfortably. It is $0.14 per M input on a cache miss, $0.0028 on a cache hit, and $0.28 output (official, July 14, 2026). For repeated-context workloads like RAG or agents that re-read a system prompt, the cache-hit rate makes it cheaper than anything else in this comparison.
Can I self-host Qwen, DeepSeek and GLM on my own hardware?
Yes, all three publish open weights. Qwen's dense 27B is the lightest and reportedly runs on around 18GB of VRAM, so a single 24GB card works. DeepSeek V4 and GLM-5.2 are large Mixture-of-Experts models that want cluster-class hardware. Serve any of them with vLLM or SGLang once you move past one machine.
Which has the biggest context window?
They cluster around 1M tokens, but don't flatten the detail. DeepSeek V4 is an official 1M, and GLM-5.2 reached 1M in mid-June 2026. Qwen's open models vary: Qwen3-Coder-Next is 256K native, while some hosted "Plus" tiers reach roughly 1M. Check the specific checkpoint you deploy rather than assuming.
Is GLM-5.2 as good as Claude or GPT for coding?
It is close on benchmarks (SWE-bench Pro 62.1 versus Claude Opus 4.8 around 69) and closer in practice than the gap suggests. In our three-week test GLM-5.2 did most of our coding work at $72/mo versus the ~$200/mo we pay for Claude Max. The tradeoff is a hard weekly cap that stopped us on day five during busy weeks.
What about Kimi K2.6, why isn't it one of the three?
Kimi (Moonshot) is real and strong. BenchLM ranks it the #2 Chinese model overall (81) and 88.7 on coding. We kept the exact three-way frame people search for, so Kimi didn't make the headline set. Think of it as the natural fourth pick on a longer open-weight shortlist, not an omission.
Which license can I use commercially?
All three. DeepSeek and GLM ship under MIT, and Qwen's open models under Apache 2.0. Every one of those is commercial-friendly. Apache 2.0 is the most permissive, with explicit patent terms, which can matter for redistribution. Always confirm the license on the Hugging Face model card for the exact model you deploy.
Qwen vs DeepSeek V4, which should I pick for an API-based product?
DeepSeek V4 for cost-sensitive, high-volume, or RAG-heavy products, thanks to the cache-hit pricing edge. Qwen when you need multilingual breadth or an Apache 2.0 license specifically. Both are available via API and both self-host, so the deciding factors are usually your token volume and your licensing constraints.
The Verdict
Don't force a single winner out of Qwen vs DeepSeek vs GLM. Tier them, then pick by job:
- GLM-5.2 for agentic coding and long-horizon agents. It's the one we ran for three weeks at $72/mo, and it earned the slot.
- DeepSeek V4 Flash for the cheapest tokens and RAG at scale, with a cache-hit price nothing else here matches.
- Qwen3 for local self-host on modest hardware, multilingual work, and the most permissive license (Apache 2.0).
The bigger lesson: read the benchmark cluster, not the ranking, and discount self-reported numbers. Freshness matters more than word count in this space, because all three ship new versions on a near-monthly cadence.
Picking the model is the easy part. Wiring it into a production stack with fallbacks, cost caps, and eval gates is where teams stall. That's what we do at Techsy, wiring an open-weight model into a production agent stack that holds up under real traffic.