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 Llama 4 Maverick for open weights, 1m context or strong image + text understanding. On a tight budget at scale, Llama 4 Maverick is the value pick.
DeepSeek V3.2 (DeepSeek, China) and Llama 4 Maverick (Meta, 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. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Llama 4 Maverick holds 7.6× more — 1M (~1,500 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: DeepSeek V3.2 is the newer model by about 8 months (released December 1, 2025), 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
Llama 4 Maverick
Provider
DeepSeek (China)
Meta (US)
Released
December 1, 2025
April 2025
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
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 core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Open weights, 1M context: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Strong image + text understanding: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Self-hostable: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Lowest cost at scale: Llama 4 Maverick — 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: Llama 4 Maverick — Its 1M window is about 7.6× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Maverick — At Open weight (self-host / free) it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Llama 4 Maverick — Larger 1M window fits more in one prompt.
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 open weights, 1m context: Llama 4 Maverick — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Maverick 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: 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.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs: needs serious hardware to self-host, and trails closed frontier on reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V3.2 (China) and Llama 4 Maverick (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Maverick is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is DeepSeek V3.2 or Llama 4 Maverick better for coding?
Public SWE-Bench figures are not available for Llama 4 Maverick, 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 Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or Llama 4 Maverick?
Llama 4 Maverick is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Maverick — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, Llama 4 Maverick 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 Llama 4 Maverick?
DeepSeek V3.2 — released December 1, 2025, about 8 months after Llama 4 Maverick.
DeepSeek V3.2 vs Llama 4 Maverick
DeepSeek · China | Meta · 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 Llama 4 Maverick for open weights, 1m context or strong image + text understanding. On a tight budget at scale, Llama 4 Maverick is the value pick.
DeepSeek V3.2 (DeepSeek, China) and Llama 4 Maverick (Meta, 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. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Llama 4 Maverick holds 7.6× more — 1M (~1,500 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: DeepSeek V3.2 is the newer model by about 8 months (released December 1, 2025), 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
Llama 4 Maverick
Provider
DeepSeek (China)
Meta (US)
Released
December 1, 2025
April 2025
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
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 core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Open weights, 1M context
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Strong image + text understanding
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Self-hostable
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Lowest cost at scale
Llama 4 Maverick
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
Llama 4 Maverick
Its 1M window is about 7.6× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Maverick
At Open weight (self-host / free) it undercuts DeepSeek V3.2, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Llama 4 Maverick
Larger 1M window fits more in one prompt.
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 open weights, 1m context
→ Llama 4 Maverick
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Maverick 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: 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.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs: needs serious hardware to self-host, and trails closed frontier on reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V3.2 (China) and Llama 4 Maverick (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Maverick is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both DeepSeek V3.2 and Llama 4 Maverick 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.
Is DeepSeek V3.2 or Llama 4 Maverick better for coding?
Public SWE-Bench figures are not available for Llama 4 Maverick, 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 Llama 4 Maverick leans toward open weights, 1m context, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or Llama 4 Maverick?
Llama 4 Maverick is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Maverick — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, Llama 4 Maverick 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 Llama 4 Maverick?
DeepSeek V3.2 — released December 1, 2025, about 8 months after Llama 4 Maverick.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.