Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. 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 V4 is the value pick.
DeepSeek V4 (DeepSeek) and GLM 4.7 (Z.ai) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Price: DeepSeek V4 is about 1.4× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.6/$2.2 per 1M tokens) — modest, but it adds up at steady volume.
Context window: DeepSeek V4 holds 4.9× more — 1M (~1,500 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.
Coding: DeepSeek V4 leads SWE-Bench Verified by 6.8 points (80.6% vs 73.8%) — a real edge on hard, real-world software tasks.
Recency: DeepSeek V4 is the newer model by about 4 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V4
GLM 4.7
Provider
DeepSeek (China)
Z.ai (China)
Released
April 24, 2026
December 22, 2025
Context window
1M (~1,500 pages)
200K (~304 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
73.8%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — It scores 80.6% on SWE-Bench Verified against GLM 4.7's 73.8% — a 6.8-point edge on real repository work.
Open MIT-licensed weights you can self-host: DeepSeek V4 — China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it leads SWE-Bench Verified 80.6% to 73.8%.
No long-context surcharge: DeepSeek V4 — Its 1M window holds about 4.9× more than GLM 4.7's 200K in a single prompt.
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; DeepSeek V4 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — DeepSeek V4 is comparatively weak here — trails the very best on hardest agentic coding
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; DeepSeek V4 does not.
Lowest cost at scale: DeepSeek V4 — At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: DeepSeek V4 — Its 1M window is about 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V4 — At $0.435/$0.87 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: DeepSeek V4 — Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — 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 V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 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 V4 and GLM 4.7 overlap enough that the right pick depends on your specific job. DeepSeek V4 costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, 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 V4 or GLM 4.7 better for coding?
On SWE-Bench Verified, DeepSeek V4 scores 80.6% and GLM 4.7 scores 73.8% — DeepSeek V4 has the measurable edge.
Which is cheaper, DeepSeek V4 or GLM 4.7?
DeepSeek V4 is cheaper — $0.435/$0.87 per 1M tokens vs $0.6/$2.2 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
DeepSeek V4 — 1M vs 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 V4 or GLM 4.7?
DeepSeek V4 — released April 24, 2026, about 4 months after GLM 4.7.
DeepSeek V4 vs GLM 4.7
DeepSeek · China | Z.ai · China · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. 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 V4 is the value pick.
DeepSeek V4 (DeepSeek) and GLM 4.7 (Z.ai) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Price: DeepSeek V4 is about 1.4× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.6/$2.2 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: DeepSeek V4 holds 4.9× more — 1M (~1,500 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.
▸Coding: DeepSeek V4 leads SWE-Bench Verified by 6.8 points (80.6% vs 73.8%) — a real edge on hard, real-world software tasks.
▸Recency: DeepSeek V4 is the newer model by about 4 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V4
GLM 4.7
Provider
DeepSeek (China)
Z.ai (China)
Released
April 24, 2026
December 22, 2025
Context window
1M (~1,500 pages)
200K (~304 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
73.8%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
It scores 80.6% on SWE-Bench Verified against GLM 4.7's 73.8% — a 6.8-point edge on real repository work.
Open MIT-licensed weights you can self-host
DeepSeek V4
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it leads SWE-Bench Verified 80.6% to 73.8%.
No long-context surcharge
DeepSeek V4
Its 1M window holds about 4.9× more than GLM 4.7's 200K in a single prompt.
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; DeepSeek V4 does not.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
DeepSeek V4 is comparatively weak here — trails the very best on hardest agentic coding
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; DeepSeek V4 does not.
Lowest cost at scale
DeepSeek V4
At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
DeepSeek V4
Its 1M window is about 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4
At $0.435/$0.87 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
→ DeepSeek V4
Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
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 V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 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 V4 and GLM 4.7 overlap enough that the right pick depends on your specific job. DeepSeek V4 costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, 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 V4 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.
On SWE-Bench Verified, DeepSeek V4 scores 80.6% and GLM 4.7 scores 73.8% — DeepSeek V4 has the measurable edge.
Which is cheaper, DeepSeek V4 or GLM 4.7?
DeepSeek V4 is cheaper — $0.435/$0.87 per 1M tokens vs $0.6/$2.2 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
DeepSeek V4 — 1M vs 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and GLM 4.7 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 V4 or GLM 4.7?
DeepSeek V4 — released April 24, 2026, about 4 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.