Pick DeepSeek V4-Flash for exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens or mit-licensed open weights — free to self-host or run via a western host. 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 V4-Flash (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 V4-Flash is deepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: DeepSeek V4-Flash is the newer model by about 16 months (released July 31, 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 V4-Flash
Llama 4 Maverick
Provider
DeepSeek (China)
Meta (US)
Released
July 31, 2026
April 2025
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.14/$0.28 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
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Exceptional value — Artificial Analysis Intelligence Index 52 at just $0.14/$0.28 per million tokens: DeepSeek V4-Flash — DeepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens — and it is the newer of the two.
MIT-licensed open weights — free to self-host or run via a Western host: DeepSeek V4-Flash — DeepSeek V4-Flash lists mIT-licensed open weights — free to self-host or run via a Western host among its strengths; Llama 4 Maverick does not.
1M-token context window: DeepSeek V4-Flash — DeepSeek V4-Flash lists 1M-token context window among its strengths; Llama 4 Maverick does not.
Open weights, 1M context: Llama 4 Maverick — Llama 4 Maverick lists open weights, 1M context among its strengths; DeepSeek V4-Flash does not.
Strong image + text understanding: Llama 4 Maverick — Llama 4 Maverick lists strong image + text understanding among its strengths; DeepSeek V4-Flash does not.
Self-hostable: Llama 4 Maverick — Llama 4 Maverick lists self-hostable among its strengths; DeepSeek V4-Flash does not.
Lowest cost at scale: Llama 4 Maverick — Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4-Flash's $0.14/$0.28 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Maverick — At Open weight (self-host / free) it undercuts DeepSeek V4-Flash, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens: DeepSeek V4-Flash — 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 V4-Flash — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4-Flash: where it fits
DeepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens. Released July 31, 2026 by DeepSeek, it is built for exceptional value — Artificial Analysis Intelligence Index 52 at just $0.14/$0.28 per million tokens, mIT-licensed open weights — free to self-host or run via a Western host, 1M-token context window, and strong coding and agentic performance for the price (DeepSeek reports 82.7 on Terminal-Bench 2.1).
Its trade-offs are real: coding/agentic figures like Terminal-Bench 82.7 are DeepSeek's own, not independently reproduced, text and code focused — not a full multimodal model, deepSeek's own hosted API stores data in China; self-host or use a Western host for privacy, and below the top frontier models on overall intelligence. At $0.14 in / $0.28 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 V4-Flash (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 V4-Flash or Llama 4 Maverick better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4-Flash leans toward exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens 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 V4-Flash or Llama 4 Maverick?
Llama 4 Maverick is cheaper — $0.14/$0.28 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both DeepSeek V4-Flash and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Flash, 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 V4-Flash or Llama 4 Maverick?
DeepSeek V4-Flash — released July 31, 2026, about 16 months after Llama 4 Maverick.
DeepSeek V4-Flash vs Llama 4 Maverick
DeepSeek · China | Meta · US · Updated June 2026
Quick verdict
Pick DeepSeek V4-Flash for exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens or mit-licensed open weights — free to self-host or run via a western host. 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 V4-Flash (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 V4-Flash is deepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: DeepSeek V4-Flash is the newer model by about 16 months (released July 31, 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 V4-Flash
Llama 4 Maverick
Provider
DeepSeek (China)
Meta (US)
Released
July 31, 2026
April 2025
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.14/$0.28 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
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Exceptional value — Artificial Analysis Intelligence Index 52 at just $0.14/$0.28 per million tokens
DeepSeek V4-Flash
DeepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens — and it is the newer of the two.
MIT-licensed open weights — free to self-host or run via a Western host
DeepSeek V4-Flash
DeepSeek V4-Flash lists mIT-licensed open weights — free to self-host or run via a Western host among its strengths; Llama 4 Maverick does not.
1M-token context window
DeepSeek V4-Flash
DeepSeek V4-Flash lists 1M-token context window among its strengths; Llama 4 Maverick does not.
Open weights, 1M context
Llama 4 Maverick
Llama 4 Maverick lists open weights, 1M context among its strengths; DeepSeek V4-Flash does not.
Strong image + text understanding
Llama 4 Maverick
Llama 4 Maverick lists strong image + text understanding among its strengths; DeepSeek V4-Flash does not.
Self-hostable
Llama 4 Maverick
Llama 4 Maverick lists self-hostable among its strengths; DeepSeek V4-Flash does not.
Lowest cost at scale
Llama 4 Maverick
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4-Flash's $0.14/$0.28 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Maverick
At Open weight (self-host / free) it undercuts DeepSeek V4-Flash, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens
→ DeepSeek V4-Flash
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 V4-Flash
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4-Flash: where it fits
DeepSeek's MIT-licensed open-weight model — arguably the best value-per-intelligence available, scoring 52 on the independent AA Index at just $0.14/$0.28 per million tokens. Released July 31, 2026 by DeepSeek, it is built for exceptional value — Artificial Analysis Intelligence Index 52 at just $0.14/$0.28 per million tokens, mIT-licensed open weights — free to self-host or run via a Western host, 1M-token context window, and strong coding and agentic performance for the price (DeepSeek reports 82.7 on Terminal-Bench 2.1).
Its trade-offs are real: coding/agentic figures like Terminal-Bench 82.7 are DeepSeek's own, not independently reproduced, text and code focused — not a full multimodal model, deepSeek's own hosted API stores data in China; self-host or use a Western host for privacy, and below the top frontier models on overall intelligence. At $0.14 in / $0.28 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 V4-Flash (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 V4-Flash 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 V4-Flash or Llama 4 Maverick better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4-Flash leans toward exceptional value — artificial analysis intelligence index 52 at just $0.14/$0.28 per million tokens 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 V4-Flash or Llama 4 Maverick?
Llama 4 Maverick is cheaper — $0.14/$0.28 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both DeepSeek V4-Flash and Llama 4 Maverick together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Flash, 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 V4-Flash or Llama 4 Maverick?
DeepSeek V4-Flash — released July 31, 2026, about 16 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.