Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. On a tight budget at scale, Hunyuan Hy3 is the value pick.
GLM 5.2 (Z.ai) and Hunyuan Hy3 (Tencent) are two of the models people most often weigh against each other in 2026. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: GLM 5.2 holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Hunyuan Hy3 is the newer model by about 23 days (released July 6, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5.2
Hunyuan Hy3
Provider
Z.ai (China)
Tencent (China)
Released
June 13, 2026
July 6, 2026
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic coding: GLM 5.2 — A core design strength of GLM 5.2.
Project-level software engineering: GLM 5.2 — A core design strength of GLM 5.2.
Tool use across long-running tasks: GLM 5.2 — A core design strength of GLM 5.2.
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — A core design strength of Hunyuan Hy3.
Runs a 295B model at the cost of a 21B — only 21B parameters active per token: Hunyuan Hy3 — A core design strength of Hunyuan Hy3.
Clean, unrestricted Apache-2.0 license with no geographic carve-out: Hunyuan Hy3 — A core design strength of Hunyuan Hy3.
Lowest cost at scale: Hunyuan Hy3 — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GLM 5.2 — Its 1M window is about 3.9× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Hunyuan Hy3 — At Open weight (self-host / free) it undercuts GLM 5.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GLM 5.2 — Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon agentic coding: GLM 5.2 — It is specifically built for that.
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — That is its strongest area.
GLM 5.2: where it fits
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Released June 13, 2026 by Z.ai, it is built for long-horizon agentic coding, project-level software engineering, tool use across long-running tasks, and tops the open-weight intelligence index (SWE-bench Pro 62.1).
Its trade-offs are real: text-only — no native multimodal input, and new release with a limited third-party track record. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
GLM 5.2 and Hunyuan Hy3 overlap enough that the right pick depends on your specific job. Hunyuan Hy3 costs less per token; GLM 5.2 holds the larger context; and each leads in its own area — GLM 5.2 for long-horizon agentic coding, Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is GLM 5.2 or Hunyuan Hy3 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.2 leans toward long-horizon agentic coding while Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.2 or Hunyuan Hy3?
Hunyuan Hy3 is cheaper — $1.4/$4.4 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
GLM 5.2 — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.2 and Hunyuan Hy3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, Hunyuan Hy3 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5.2 or Hunyuan Hy3?
Hunyuan Hy3 — released July 6, 2026, about 23 days after GLM 5.2.
GLM 5.2 vs Hunyuan Hy3
Z.ai · China | Tencent · China · Updated June 2026
Quick verdict
Pick GLM 5.2 for long-horizon agentic coding or project-level software engineering. Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. On a tight budget at scale, Hunyuan Hy3 is the value pick.
GLM 5.2 (Z.ai) and Hunyuan Hy3 (Tencent) are two of the models people most often weigh against each other in 2026. GLM 5.2 is an open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: GLM 5.2 holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Hunyuan Hy3 is the newer model by about 23 days (released July 6, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5.2
Hunyuan Hy3
Provider
Z.ai (China)
Tencent (China)
Released
June 13, 2026
July 6, 2026
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
$1.4/$4.4 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic coding
GLM 5.2
A core design strength of GLM 5.2.
Project-level software engineering
GLM 5.2
A core design strength of GLM 5.2.
Tool use across long-running tasks
GLM 5.2
A core design strength of GLM 5.2.
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost
Hunyuan Hy3
A core design strength of Hunyuan Hy3.
Runs a 295B model at the cost of a 21B — only 21B parameters active per token
Hunyuan Hy3
A core design strength of Hunyuan Hy3.
Clean, unrestricted Apache-2.0 license with no geographic carve-out
Hunyuan Hy3
A core design strength of Hunyuan Hy3.
Lowest cost at scale
Hunyuan Hy3
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GLM 5.2
Its 1M window is about 3.9× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Hunyuan Hy3
At Open weight (self-host / free) it undercuts GLM 5.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 5.2
Larger 1M window fits more in one prompt.
Anyone whose priority is long-horizon agentic coding
→ GLM 5.2
It is specifically built for that.
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost
→ Hunyuan Hy3
That is its strongest area.
GLM 5.2: where it fits
An open-weight reasoning model built for long-horizon coding and multi-step agent workflows — strong and cheap. Released June 13, 2026 by Z.ai, it is built for long-horizon agentic coding, project-level software engineering, tool use across long-running tasks, and tops the open-weight intelligence index (SWE-bench Pro 62.1).
Its trade-offs are real: text-only — no native multimodal input, and new release with a limited third-party track record. At $1.4 in / $4.4 out per million tokens, it sits in the mid price band.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
GLM 5.2 and Hunyuan Hy3 overlap enough that the right pick depends on your specific job. Hunyuan Hy3 costs less per token; GLM 5.2 holds the larger context; and each leads in its own area — GLM 5.2 for long-horizon agentic coding, Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both GLM 5.2 and Hunyuan Hy3 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, GLM 5.2 leans toward long-horizon agentic coding while Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, GLM 5.2 or Hunyuan Hy3?
Hunyuan Hy3 is cheaper — $1.4/$4.4 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
GLM 5.2 — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5.2 and Hunyuan Hy3 together?
Yes — a multi-model platform like LumiChats gives you GLM 5.2, Hunyuan Hy3 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, GLM 5.2 or Hunyuan Hy3?
Hunyuan Hy3 — released July 6, 2026, about 23 days after GLM 5.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.