Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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. On a tight budget at scale, DeepSeek R1 is the value pick.
DeepSeek R1 (DeepSeek) and GLM 4.7 (Z.ai) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: nearly identical — $0.55/$2.19 per 1M tokens vs $0.6/$2.2 per 1M tokens. Cost will not be the deciding factor here.
Context window: GLM 4.7 holds 1.6× more — 200K (~304 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: GLM 4.7 is the newer model by about 11 months (released December 22, 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
GLM 4.7
Provider
DeepSeek (China)
Z.ai (China)
Released
January 2025
December 22, 2025
Context window
128K (~192 pages)
200K (~304 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.6/$2.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
73.8%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model: DeepSeek R1 — GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
Transparent chain-of-thought: DeepSeek R1 — The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.
Low cost: DeepSeek R1 — At $0.55/$2.19 per 1M tokens it undercuts GLM 4.7 ($0.6/$2.2 per 1M tokens), and that gap compounds at volume.
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it carries the larger 200K context.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it is the newer of the two.
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — Its 200K window holds about 1.6× more than DeepSeek R1's 128K in a single prompt.
Lowest cost at scale: DeepSeek R1 — At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GLM 4.7 — Its 200K window is about 1.6× larger than DeepSeek R1's 128K, fitting roughly 304 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek R1 — At $0.55/$2.19 per 1M tokens it undercuts GLM 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GLM 4.7 — Larger 200K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model: DeepSeek R1 — It is specifically built for that.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
DeepSeek R1 and GLM 4.7 overlap enough that the right pick depends on your specific job. DeepSeek R1 costs less per token; GLM 4.7 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek R1 or GLM 4.7 better for coding?
Public SWE-Bench figures are not available for DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or GLM 4.7?
DeepSeek R1 is cheaper — $0.55/$2.19 per 1M tokens vs $0.6/$2.2 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
GLM 4.7 — 200K vs 128K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, GLM 4.7 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, DeepSeek R1 or GLM 4.7?
GLM 4.7 — released December 22, 2025, about 11 months after DeepSeek R1.
DeepSeek R1 vs GLM 4.7
DeepSeek · China | Z.ai · China · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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. On a tight budget at scale, DeepSeek R1 is the value pick.
DeepSeek R1 (DeepSeek) and GLM 4.7 (Z.ai) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: nearly identical — $0.55/$2.19 per 1M tokens vs $0.6/$2.2 per 1M tokens. Cost will not be the deciding factor here.
▸Context window: GLM 4.7 holds 1.6× more — 200K (~304 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: GLM 4.7 is the newer model by about 11 months (released December 22, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
GLM 4.7
Provider
DeepSeek (China)
Z.ai (China)
Released
January 2025
December 22, 2025
Context window
128K (~192 pages)
200K (~304 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.6/$2.2 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
73.8%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model
DeepSeek R1
GLM 4.7 is comparatively weak here — text-only with no vision, and self-hosting a 358B model is a serious hardware commitment
Transparent chain-of-thought
DeepSeek R1
The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.
Low cost
DeepSeek R1
At $0.55/$2.19 per 1M tokens it undercuts GLM 4.7 ($0.6/$2.2 per 1M tokens), and that gap compounds at volume.
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it carries the larger 200K context.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it is the newer of the two.
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
Its 200K window holds about 1.6× more than DeepSeek R1's 128K in a single prompt.
Lowest cost at scale
DeepSeek R1
At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GLM 4.7
Its 200K window is about 1.6× larger than DeepSeek R1's 128K, fitting roughly 304 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek R1
At $0.55/$2.19 per 1M tokens it undercuts GLM 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GLM 4.7
Larger 200K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
It is specifically built for that.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
DeepSeek R1 and GLM 4.7 overlap enough that the right pick depends on your specific job. DeepSeek R1 costs less per token; GLM 4.7 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek R1 and GLM 4.7 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 DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or GLM 4.7?
DeepSeek R1 is cheaper — $0.55/$2.19 per 1M tokens vs $0.6/$2.2 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
GLM 4.7 — 200K vs 128K, about 1.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, GLM 4.7 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, DeepSeek R1 or GLM 4.7?
GLM 4.7 — released December 22, 2025, about 11 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.