DeepSeek V4 vs GLM-5.2 vs Qwen: 10x Price Gap (Tech Insider)

An aggregator comparison of three open-weight Chinese families — DeepSeek V4, Z.ai‘s GLM-5.2, and Alibaba’s Qwen3.6 — published 2026-07-27, bylined Nadia Dubois. T4, and the tier matters here more than usual.

Why T4

The page carries an affiliate leaderboard ad (trovedrops.com with sub_id tracking params), a Google Preferred Sources opt-in widget, and internal links to sibling listicles — including one titled “Opus 4.8 vs GPT-5.6 vs Gemini 3.1 Pro: $18 Price Gap [2026]”, the identical X vs Y vs Z: $N Price Gap [2026] template. That’s a content template stamped across model pairings, monetised by referral, not an analysis someone needed to write. No methodology is given for any figure and no benchmark is linked to its scoreboard.

Fetched via Firecrawl — the site 403s WebFetch.

What it claims

The 10x of the headline is DeepSeek’s cheap tier against GLM’s: V4 Flash at $0.14 / $0.28 versus GLM-5.2’s “list price” of $1.40, input side only. Also claimed: DeepSeek cache hits near $0.0036 / 1M; third-party resellers median ~$0.55 / $1.85 for GLM-5.2; a flat $72/month Z.ai Coding Plan Pro; DeepSeek V4 Pro at 93.5% on LiveCodeBench; GLM-5.2 ahead of DeepSeek by 6.7 points on SWE-bench Pro and 17 points on long-horizon agentic coding; a 27B Qwen dense variant running on a single 18 GB consumer GPU. Release dates given as DeepSeek V4 Pro 2026-04-24 and GLM-5.2 2026-06-13.

Checked against what this wiki already holds

The useful part of ingesting a T4 source into a corpus with T1/T2 on the same subjects is that it can be marked.

Corroborated. GLM-5.2 at 753B MoE / ~40B active, 1M context, MIT matches glm-52 exactly (T2, simon-willison citing first-party release facts). The deepseek-v4-flash / deepseek-v4-pro split and context caching match deepseek-api-docs (T1, the vendor’s own reference). So the spec sheet is broadly sound where it can be checked.

Contradicted — the release date. GLM-5.2 shipped 2026-06-16 per glm-52; this article says June 13. Three days, and nothing rests on it, but it’s the signature of aggregation without a primary check.

Contradicted — and it’s the headline. This article presents $1.40 / $4.40 as GLM-5.2’s vendor list price, then separately reports third-party resale at ~$0.55 / $1.85. glm-52 attributes $1.40 / $4.40 to OpenRouter hosts — i.e. to resale. Both can’t be right about what that number is, and the whole “10x price gap” depends on $1.40 being Z.ai’s own rate. If Willison’s attribution is correct, the article is comparing DeepSeek’s vendor price against GLM’s reseller price and calling the difference a vendor price gap. Recorded as a conflict, not resolved — settling it needs Z.ai’s pricing page, which neither source is.

Unverifiable here. The benchmark numbers (93.5% LiveCodeBench, the 6.7- and 17-point margins) cite no scoreboard and no evaluation date. The one independent ranking this wiki holds points the other way on overall capability: artificial-analysis Intelligence Index v4.1 puts GLM-5.2 at 51 and DeepSeek V4 Pro at 44 (glm-52). Not a direct contradiction — different benchmarks measure different things — but a reader taking “DeepSeek posts the highest publicly tested score” as a general capability claim would be misled.

What it’s still good for

Two things survive the tier. It’s a snapshot of how the open-weight price floor is being marketed in mid-2026 — three Chinese labs, two MIT and one Apache-2.0, all pitched on undercutting closed frontier APIs, which is the open-weight wedge thesis showing up in SEO copy. And its resale layer point is real and under-covered here: independent hosts serving the same open weights below vendor list means an open-weight model’s price is a market, not a number (llm-api-pricing). That the arbitrage exists is a structural consequence of open weights; the specific medians quoted are unsourced.

Related

deepseek · glm-52 · qwen · z-ai · llm-api-pricing · open-weight-models · llm-benchmarks · artificial-analysis · deepseek-api-docs · synthesis