Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. 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 4.7 (Z.ai) and Hunyuan Hy3 (Tencent) are two of the models people most often weigh against each other in 2026. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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: Hunyuan Hy3 holds 1.3× more — 256K (~384 pages) vs 200K (~304 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 7 months (released July 6, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 4.7
Hunyuan Hy3
Provider
Z.ai (China)
Tencent (China)
Released
December 22, 2025
July 6, 2026
Context window
200K (~304 pages)
256K (~384 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — GLM 4.7 lists genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions among its strengths; Hunyuan Hy3 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — Hunyuan Hy3 is comparatively weak here — benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation among its strengths; Hunyuan Hy3 does not.
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost: Hunyuan Hy3 — A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it carries the larger 256K context.
Runs a 295B model at the cost of a 21B — only 21B parameters active per token: Hunyuan Hy3 — Its 256K window holds about 1.3× more than GLM 4.7's 200K in a single prompt.
Clean, unrestricted Apache-2.0 license with no geographic carve-out: Hunyuan Hy3 — A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it is the newer of the two.
Lowest cost at scale: Hunyuan Hy3 — Its weights are open, so at volume you pay for your own hardware instead of GLM 4.7's $0.6/$2.2 per 1M tokens.
Largest single-prompt input: Hunyuan Hy3 — Its 256K window is about 1.3× larger than GLM 4.7's 200K, fitting roughly 384 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 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Hunyuan Hy3 — Larger 256K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — 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 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget 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 4.7 and Hunyuan Hy3 overlap enough that the right pick depends on your specific job. Hunyuan Hy3 costs less per token; Hunyuan Hy3 holds the larger context; and each leads in its own area — GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, 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 4.7 or Hunyuan Hy3 better for coding?
Public SWE-Bench figures are not available for Hunyuan Hy3, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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 4.7 or Hunyuan Hy3?
Hunyuan Hy3 is cheaper — $0.6/$2.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Hunyuan Hy3 — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and Hunyuan Hy3 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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 4.7 or Hunyuan Hy3?
Hunyuan Hy3 — released July 6, 2026, about 7 months after GLM 4.7.
GLM 4.7 vs Hunyuan Hy3
Z.ai · China | Tencent · China · Updated June 2026
Quick verdict
Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. 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 4.7 (Z.ai) and Hunyuan Hy3 (Tencent) are two of the models people most often weigh against each other in 2026. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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: Hunyuan Hy3 holds 1.3× more — 256K (~384 pages) vs 200K (~304 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 7 months (released July 6, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 4.7
Hunyuan Hy3
Provider
Z.ai (China)
Tencent (China)
Released
December 22, 2025
July 6, 2026
Context window
200K (~304 pages)
256K (~384 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.8%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
GLM 4.7 lists genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions among its strengths; Hunyuan Hy3 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
Hunyuan Hy3 is comparatively weak here — benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
GLM 4.7 lists an unusually generous 128K maximum output, which suits bulk refactors and long generation among its strengths; Hunyuan Hy3 does not.
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost
Hunyuan Hy3
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it carries the larger 256K context.
Runs a 295B model at the cost of a 21B — only 21B parameters active per token
Hunyuan Hy3
Its 256K window holds about 1.3× more than GLM 4.7's 200K in a single prompt.
Clean, unrestricted Apache-2.0 license with no geographic carve-out
Hunyuan Hy3
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost — and it is the newer of the two.
Lowest cost at scale
Hunyuan Hy3
Its weights are open, so at volume you pay for your own hardware instead of GLM 4.7's $0.6/$2.2 per 1M tokens.
Largest single-prompt input
Hunyuan Hy3
Its 256K window is about 1.3× larger than GLM 4.7's 200K, fitting roughly 384 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 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Hunyuan Hy3
Larger 256K window fits more in one prompt.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
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 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget 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 4.7 and Hunyuan Hy3 overlap enough that the right pick depends on your specific job. Hunyuan Hy3 costs less per token; Hunyuan Hy3 holds the larger context; and each leads in its own area — GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, 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 4.7 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 Hunyuan Hy3, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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 4.7 or Hunyuan Hy3?
Hunyuan Hy3 is cheaper — $0.6/$2.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Hunyuan Hy3 — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and Hunyuan Hy3 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, 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 4.7 or Hunyuan Hy3?
Hunyuan Hy3 — released July 6, 2026, about 7 months after GLM 4.7.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.