Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. Choose DeepSeek V3.2 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
DeepSeek V3.2 (DeepSeek, China) and GPT-6 Luna (OpenAI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GPT-6 Luna is about 2.8× cheaper on input ($0.1/$0.5 per 1M tokens vs $0.28/$0.42 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: GPT-6 Luna holds 8× more — 1.05M tokens (~1,575 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: GPT-6 Luna is the newer model by about 10 months (released September 22, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
DeepSeek V3.2
GPT-6 Luna
Provider
DeepSeek (China)
OpenAI (US)
Released
December 1, 2025
September 22, 2026
Context window
131K (~197 pages)
1.05M tokens (~1,575 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.1/$0.5 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and its weights are open while GPT-6 Luna is API-only.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — DeepSeek V3.2 lists agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes) among its strengths; GPT-6 Luna does not.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — GPT-6 Luna is comparatively weak here — less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — At $0.1/$0.5 per 1M tokens it undercuts DeepSeek V3.2 ($0.28/$0.42 per 1M tokens), and that gap compounds at volume.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it runs cheaper at $0.1/$0.5 per 1M tokens.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family: GPT-6 Luna — OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Lowest cost at scale: GPT-6 Luna — At $0.1/$0.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: GPT-6 Luna — Its 1.05M tokens window is about 8× larger than DeepSeek V3.2's 131K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GPT-6 Luna — At $0.1/$0.5 per 1M tokens it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: GPT-6 Luna — Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs: DeepSeek V3.2 — Open weights let you run it on your own hardware; GPT-6 Luna is API-only.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa): DeepSeek V3.2 — It is specifically built for that.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume: GPT-6 Luna — That is its strongest area.
An enterprise with regional data-residency rules: GPT-6 Luna or DeepSeek V3.2 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V3.2 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Luna gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is DeepSeek V3.2 or GPT-6 Luna better for coding?
Public SWE-Bench figures are not available for GPT-6 Luna, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or GPT-6 Luna?
DeepSeek V3.2 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-6 Luna — 1.05M tokens vs 131K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and GPT-6 Luna together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, GPT-6 Luna 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 V3.2 or GPT-6 Luna?
GPT-6 Luna — released September 22, 2026, about 10 months after DeepSeek V3.2.
DeepSeek V3.2 vs GPT-6 Luna
DeepSeek · China | OpenAI · US · Updated June 2026
Quick verdict
Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). Pick GPT-6 Luna for openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume or available to free and go plan users in the chatgpt desktop app, not gated to paid tiers. Choose DeepSeek V3.2 if you need self-hosting or data privacy; GPT-6 Luna if you want a managed API.
DeepSeek V3.2 (DeepSeek, China) and GPT-6 Luna (OpenAI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. GPT-6 Luna is openAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: GPT-6 Luna is about 2.8× cheaper on input ($0.1/$0.5 per 1M tokens vs $0.28/$0.42 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: GPT-6 Luna holds 8× more — 1.05M tokens (~1,575 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: GPT-6 Luna is the newer model by about 10 months (released September 22, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
DeepSeek V3.2
GPT-6 Luna
Provider
DeepSeek (China)
OpenAI (US)
Released
December 1, 2025
September 22, 2026
Context window
131K (~197 pages)
1.05M tokens (~1,575 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.1/$0.5 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and its weights are open while GPT-6 Luna is API-only.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
DeepSeek V3.2 lists agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes) among its strengths; GPT-6 Luna does not.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
GPT-6 Luna is comparatively weak here — less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems
OpenAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
GPT-6 Luna
At $0.1/$0.5 per 1M tokens it undercuts DeepSeek V3.2 ($0.28/$0.42 per 1M tokens), and that gap compounds at volume.
Available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it runs cheaper at $0.1/$0.5 per 1M tokens.
Cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family
GPT-6 Luna
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth — and it carries the larger 1.05M tokens context.
Lowest cost at scale
GPT-6 Luna
At $0.1/$0.5 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
GPT-6 Luna
Its 1.05M tokens window is about 8× larger than DeepSeek V3.2's 131K, fitting roughly 1,575 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GPT-6 Luna
At $0.1/$0.5 per 1M tokens it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ GPT-6 Luna
Larger 1.05M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ DeepSeek V3.2
Open weights let you run it on your own hardware; GPT-6 Luna is API-only.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
It is specifically built for that.
Anyone whose priority is openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume
→ GPT-6 Luna
That is its strongest area.
An enterprise with regional data-residency rules
→ GPT-6 Luna or DeepSeek V3.2
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
GPT-6 Luna: where it fits
OpenAI's cheapest GPT-6 tier, released September 22, 2026 alongside GPT-6 Sol — $0.10/$0.50 per million tokens, built for speed over depth. Released September 22, 2026 by OpenAI, it is built for openAI's cheapest GPT-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, available to Free and Go plan users in the ChatGPT desktop app, not gated to paid tiers, cache reads at $0.01/MTok — a 90% discount, the cheapest in the GPT-6 family, and 1.05M-token context window carried down from the flagship tier.
Its trade-offs: less reasoning capability than sibling GPT-6 Sol — built for speed and volume, not hard problems, and as the entry-level tier, expect it to be the first model swapped out when OpenAI ships the next cost-tier refresh. At $0.1 in / $0.5 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V3.2 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. GPT-6 Luna gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both DeepSeek V3.2 and GPT-6 Luna 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 GPT-6 Luna, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while GPT-6 Luna leans toward openai's cheapest gpt-6 tier — $0.10/$0.50 per million tokens, optimized for fast responses at high volume, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or GPT-6 Luna?
DeepSeek V3.2 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Luna is API-metered at $0.1/$0.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
GPT-6 Luna — 1.05M tokens vs 131K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and GPT-6 Luna together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, GPT-6 Luna 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 V3.2 or GPT-6 Luna?
GPT-6 Luna — released September 22, 2026, about 10 months after DeepSeek V3.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.