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 Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total) or near-31b-dense quality at a fraction of the compute and memory-bandwidth cost. On a tight budget at scale, DeepSeek V4-Flash is the value pick.
DeepSeek V4-Flash (DeepSeek, China) and Gemma 4 26B A4B (Google, 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. Gemma 4 26B A4B is an Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: nearly identical — $0.14/$0.28 per 1M tokens vs $0.15/$0.6 per 1M tokens. Cost will not be the deciding factor here.
Context window: DeepSeek V4-Flash holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 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 V4-Flash is the newer model by about 4 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
Gemma 4 26B A4B
Provider
DeepSeek (China)
Google (US)
Released
July 31, 2026
April 2, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.14/$0.28 per 1M tokens
$0.15/$0.6 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, 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 — At $0.14/$0.28 per 1M tokens it undercuts Gemma 4 26B A4B ($0.15/$0.6 per 1M tokens), and that gap compounds at volume.
MIT-licensed open weights — free to self-host or run via a Western host: 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 runs cheaper at $0.14/$0.28 per 1M tokens.
1M-token context window: DeepSeek V4-Flash — Its 1M window holds about 3.8× more than Gemma 4 26B A4B's 256K in a single prompt.
Fast, cheap inference from a sparse MoE (3.8B active of 25.2B total): Gemma 4 26B A4B — Gemma 4 26B A4B lists fast, cheap inference from a sparse MoE (3.8B active of 25.2B total) among its strengths; DeepSeek V4-Flash does not.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost: Gemma 4 26B A4B — Gemma 4 26B A4B lists near-31B-dense quality at a fraction of the compute and memory-bandwidth cost among its strengths; DeepSeek V4-Flash does not.
Strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6): Gemma 4 26B A4B — DeepSeek V4-Flash is comparatively weak here — coding/agentic figures like Terminal-Bench 82.7 are DeepSeek's own, not independently reproduced
Lowest cost at scale: DeepSeek V4-Flash — At $0.14/$0.28 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-Flash — Its 1M window is about 3.8× larger than Gemma 4 26B A4B's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V4-Flash — At $0.14/$0.28 per 1M tokens it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4-Flash — Larger 1M window fits more in one prompt.
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 fast, cheap inference from a sparse moe (3.8b active of 25.2b total): Gemma 4 26B A4B — That is its strongest area.
An enterprise with regional data-residency rules: Gemma 4 26B A4B 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.
Gemma 4 26B A4B: where it fits
An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Released April 2, 2026 by Google, it is built for fast, cheap inference from a sparse MoE (3.8B active of 25.2B total), near-31B-dense quality at a fraction of the compute and memory-bandwidth cost, strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6), and multimodal input (text/image, plus video processed as frames up to 60s) with native function calling.
Its trade-offs: all 25.2B parameters must be loaded into memory even though only 3.8B are active per token, and 256K context trails 1M-token frontier rivals, and this variant has no audio input (audio is E2B/E4B/12B only). At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4-Flash (China) and Gemma 4 26B A4B (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. DeepSeek V4-Flash 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 Gemma 4 26B A4B 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 Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4-Flash or Gemma 4 26B A4B?
DeepSeek V4-Flash is cheaper — $0.14/$0.28 per 1M tokens vs $0.15/$0.6 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
DeepSeek V4-Flash — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4-Flash and Gemma 4 26B A4B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Flash, Gemma 4 26B A4B 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 Gemma 4 26B A4B?
DeepSeek V4-Flash — released July 31, 2026, about 4 months after Gemma 4 26B A4B.
DeepSeek V4-Flash vs Gemma 4 26B A4B
DeepSeek · China | Google · 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 Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total) or near-31b-dense quality at a fraction of the compute and memory-bandwidth cost. On a tight budget at scale, DeepSeek V4-Flash is the value pick.
DeepSeek V4-Flash (DeepSeek, China) and Gemma 4 26B A4B (Google, 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. Gemma 4 26B A4B is an Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: nearly identical — $0.14/$0.28 per 1M tokens vs $0.15/$0.6 per 1M tokens. Cost will not be the deciding factor here.
▸Context window: DeepSeek V4-Flash holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 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 V4-Flash is the newer model by about 4 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
Gemma 4 26B A4B
Provider
DeepSeek (China)
Google (US)
Released
July 31, 2026
April 2, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.14/$0.28 per 1M tokens
$0.15/$0.6 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, 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
At $0.14/$0.28 per 1M tokens it undercuts Gemma 4 26B A4B ($0.15/$0.6 per 1M tokens), and that gap compounds at volume.
MIT-licensed open weights — free to self-host or run via a Western host
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 runs cheaper at $0.14/$0.28 per 1M tokens.
1M-token context window
DeepSeek V4-Flash
Its 1M window holds about 3.8× more than Gemma 4 26B A4B's 256K in a single prompt.
Fast, cheap inference from a sparse MoE (3.8B active of 25.2B total)
Gemma 4 26B A4B
Gemma 4 26B A4B lists fast, cheap inference from a sparse MoE (3.8B active of 25.2B total) among its strengths; DeepSeek V4-Flash does not.
Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost
Gemma 4 26B A4B
Gemma 4 26B A4B lists near-31B-dense quality at a fraction of the compute and memory-bandwidth cost among its strengths; DeepSeek V4-Flash does not.
DeepSeek V4-Flash is comparatively weak here — coding/agentic figures like Terminal-Bench 82.7 are DeepSeek's own, not independently reproduced
Lowest cost at scale
DeepSeek V4-Flash
At $0.14/$0.28 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-Flash
Its 1M window is about 3.8× larger than Gemma 4 26B A4B's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4-Flash
At $0.14/$0.28 per 1M tokens it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4-Flash
Larger 1M window fits more in one prompt.
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 fast, cheap inference from a sparse moe (3.8b active of 25.2b total)
→ Gemma 4 26B A4B
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemma 4 26B A4B 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.
Gemma 4 26B A4B: where it fits
An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Released April 2, 2026 by Google, it is built for fast, cheap inference from a sparse MoE (3.8B active of 25.2B total), near-31B-dense quality at a fraction of the compute and memory-bandwidth cost, strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6), and multimodal input (text/image, plus video processed as frames up to 60s) with native function calling.
Its trade-offs: all 25.2B parameters must be loaded into memory even though only 3.8B are active per token, and 256K context trails 1M-token frontier rivals, and this variant has no audio input (audio is E2B/E4B/12B only). At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4-Flash (China) and Gemma 4 26B A4B (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. DeepSeek V4-Flash 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 Gemma 4 26B A4B 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 Gemma 4 26B A4B 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 Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4-Flash or Gemma 4 26B A4B?
DeepSeek V4-Flash is cheaper — $0.14/$0.28 per 1M tokens vs $0.15/$0.6 per 1M tokens, roughly 1.1× apart on input.
Which has the bigger context window?
DeepSeek V4-Flash — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4-Flash and Gemma 4 26B A4B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Flash, Gemma 4 26B A4B 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 Gemma 4 26B A4B?
DeepSeek V4-Flash — released July 31, 2026, about 4 months after Gemma 4 26B A4B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.