{"id":609,"date":"2026-08-28T07:30:04","date_gmt":"2026-08-28T07:30:04","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=609"},"modified":"2026-08-28T07:30:06","modified_gmt":"2026-08-28T07:30:06","slug":"llama-4-scout-vs-deepseek-v4-flash","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/llama-4-scout-vs-deepseek-v4-flash","title":{"rendered":"Llama 4 Scout vs DeepSeek V4 Flash: Specs, Price, Side-by-Side Tests (2026)"},"content":{"rendered":"\n\n\n
\n\n

Comparison \u00b7 August 2026<\/p>\n\n

\nLlama 4 Scout (Meta API) and DeepSeek V4 Flash (DeepSeek API) are two cheap 1M-plus MoE models teams actually route for volume work in 2026. Scout is a Llama 4 17B Instruct 16E cut with image input and a slightly larger window. Flash is DeepSeek\u2019s efficiency MoE (13B active of 284B total), text-only on this card, with the cheaper meter. This is a decision guide, not a leaderboard dump: published API rates, three workload cost scenarios, live writing and coding snippets, and a pick matrix you can rerun. If you want both without juggling tabs, use a\nmulti-model AI workspace<\/a>\nor start on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Llama 4 Scout if:<\/strong> a screenshot is in the prompt, you want the slightly larger 1.31M window, and you want a Moon-cheese refuse that stops instead of mining cheese.<\/p>\n

Pick DeepSeek V4 Flash if:<\/strong> the job is text-only and you want the cheaper loop. Our agent-loop estimate is $0.0142 vs $0.0233, with a complete email and a complete empty-list guard in this pack.<\/p>\n

Best default for many teams:<\/strong> Flash for cheap text. Scout when an image lands or trust-stop matters. Route. Do not crown a permanent overall winner.<\/p>\n

Data checked: 2026-08-26. Prices and model cards change. Verify live API test and vendor pages.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1.31M<\/strong><\/p><\/td>\n

Llama 4 Scout context (API)<\/p><\/td>\n<\/tr>\n

1.05M<\/strong><\/p><\/td>\n

DeepSeek V4 Flash context (API)<\/p><\/td>\n<\/tr>\n

$0.11 \/ $0.34<\/strong><\/p><\/td>\n

Llama 4 Scout input\/output per 1M tokens (API pricing, 2026-08-26)<\/p><\/td>\n<\/tr>\n

$0.0886 \/ $0.177<\/strong><\/p><\/td>\n

DeepSeek V4 Flash input\/output per 1M tokens (API pricing, 2026-08-26)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (context, image input, writing completeness, coding micro-test, cost efficiency). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n\n

You are\u2026<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ marketer<\/p><\/td>\n

DeepSeek V4 Flash (this run)<\/p><\/td>\n

Flash wrote a complete note with the Acme line. Scout leaked a rewrite preamble and truncated.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

Flash for cheap text agents; Scout if images land<\/p><\/td>\n

Both named the empty-list bug. Flash returned a complete guard plus a test print. Scout\u2019s capture truncated on return None<\/code>.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

Llama 4 Scout when screenshots matter<\/p><\/td>\n

Scout 1,310,720 vs Flash 1,048,576. Only Scout lists image input. Flash is text-only on this card.<\/p><\/td>\n<\/tr>\n

Budget \/ high volume API<\/p><\/td>\n

DeepSeek V4 Flash<\/p><\/td>\n

Chat $0.00018 vs $0.00028. Repo $0.00780 vs $0.0102. Agent $0.0142 vs $0.0233.<\/p><\/td>\n<\/tr>\n

Trust \/ false-premise jobs<\/p><\/td>\n

Llama 4 Scout (this run)<\/p><\/td>\n

Scout refused Moon-cheese and stopped. Flash refused, then built a cheese-mining plan.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

This page compares two specific API models, not vague \u201cLlama vs DeepSeek\u201d brands. Sibling Maverick pair:\nLlama 4 Maverick vs DeepSeek V4 Flash<\/a>.\nSibling Flash pair:\nDeepSeek V4 Flash vs Gemini 3.7 Flash<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

Llama 4 Scout<\/p><\/th>\n

DeepSeek V4 Flash<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

Meta<\/p><\/td>\n

DeepSeek<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

Llama 4 Scout (Meta API)<\/code><\/p><\/td>\n

DeepSeek V4 Flash (DeepSeek API)<\/code><\/p><\/td>\n<\/tr>\n

Listed card name<\/p><\/td>\n

Meta: Llama 4 Scout<\/p><\/td>\n

DeepSeek: DeepSeek V4 Flash 0423<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

Llama 4 Scout 17B Instruct (16E) MoE; 17B active of 109B total<\/p><\/td>\n

Efficiency MoE: 284B total, 13B activated; Flash lane<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Llama 4 family also appears in Meta apps and hosted APIs; this article uses the API card above<\/p><\/td>\n

Also in DeepSeek products; this article uses the Flash 0423 API card above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

If a page still compares Llama 3 or DeepSeek V3 without these labels, treat it as historical. For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API card, 2026-08-26)<\/h2>\n\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

Llama 4 Scout<\/p><\/th>\n

DeepSeek V4 Flash<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,310,720 tokens<\/p><\/td>\n

1,048,576 tokens<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text, image<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Architecture (card)<\/p><\/td>\n

MoE, 16 experts, 17B active of 109B total<\/p><\/td>\n

MoE, 284B total, 13B activated; fast-inference positioning<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

Yes (Llama 4 Instruct family; confirm the hosted build)<\/p><\/td>\n

Not specified on this API card<\/p><\/td>\n<\/tr>\n

Vendor positioning (card)<\/p><\/td>\n

Native multimodal Llama 4 Instruct (16E)<\/p><\/td>\n

Efficiency-optimized MoE with a 1M window; designed for fast inference<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We did not invent max-output, reasoning-mode, or realtime-search rows. Those fields were not on the cards we pulled. Both windows are 1M-class. Scout is larger (1,310,720 vs 1,048,576) and lists image. Flash is text-only on this card and cheaper. If your product depends on a specific tool or effort switch, confirm it on the live vendor page before you ship.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

List prices are easy to game. Workload cost is what you feel. Numbers below use published per-million rates as of 2026-08-26<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

Llama 4 Scout<\/p><\/th>\n

DeepSeek V4 Flash<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$0.11<\/p><\/td>\n

$0.0886<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$0.34<\/p><\/td>\n

$0.177<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$0.055<\/p><\/td>\n

$0.0177<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Flash is cheaper on every sticker: input, output, and cache. Scout is not expensive. It is just not the cheaper of these two. The live reasons to pay Scout are image input and a slightly larger window, not a different price class.<\/p>\n\n\n\n\n\n\n

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. Llama 4 Scout<\/p><\/th>\n

Est. DeepSeek V4 Flash<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.00028<\/p><\/td>\n

$0.00018<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$0.0102<\/p><\/td>\n

$0.00780<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$0.0233<\/p><\/td>\n

$0.0142<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-08-26). Chat is 1k in + 0.5k out. Repo review is 80k in + 4k out. Agent loop is 200k in with 50% cache hits + 20k out. Chart: i10X.<\/figcaption>\n<\/figure>\n

Flash wins every stylized workload we priced: chat $0.00018 vs $0.00028, repo $0.00780 vs $0.0102, cached agent $0.0142 vs $0.0233. Pay Scout when a screenshot is the reason. For subscription stacks, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (not one score)<\/h2>\n

Public benches disagree by harness, effort mode, and date. We are not inventing index numbers we do not have for this pair. Treat vendor cards as positioning, treat our snippets as a small live pack, and prefer your own side-by-side on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Scout\u2019s card is a native-multimodal Llama 4 Instruct 16E. Flash\u2019s card is an efficiency MoE aimed at fast inference. Those are positioning lines, not benches. Our micro-test split on completeness: both named ZeroDivisionError<\/code>. Flash shipped if not nums: return 0<\/code> plus print(average([]))<\/code>. Scout explained the bug, returned None<\/code>, then truncated. For volume text loops, Flash\u2019s rates win. For agents that ingest screenshots, Scout is the card that lists image.<\/p>\n\n

Writing and tone<\/h3>\n

Flash won this run on completeness. It wrote \u201cHi there,\u201d kept Tuesday \/ Friday \/ Wednesday \/ Acme, and closed \u201cThanks so much!\u201d in one paragraph. Scout announced a rewritten version, added \u201cI hope you’re doing well!,\u201d then our capture cut at the competitive slide. If your brand wants a paste-ready internal ping, Flash was closer. If you want more greeting energy and you will wait for the full output, Scout is the warmer draft once you strip the first line.<\/p>\n\n

Research, math, reasoning<\/h3>\n

We did not run a science-QA pack, so we will not fake one. Both refused the green-cheese Moon. Scout stopped: silicate minerals, no cheese, no need for a protein-mining plan on that premise. Flash refused, then harvested cheese with drills and a centrifuge for casein. That is a trust split, not a math proof. For publishable work, add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

Both are 1M-class. Scout is larger: 1,310,720 vs 1,048,576. The modality split is the real one. Scout lists text and image. Flash lists text only. Giant text packs can sit on either. Screenshots go to Scout. We did not run a vision eval, so Figure 1\u2019s image bar is a card fact, not a quality ranking. Neither card we pulled lists audio or video.<\/p>\n\n

Speed<\/h3>\n

No tok\/s in this pack. Flash\u2019s card says fast inference. That is positioning, not a measured p50. Measure from your API region. Do not ship on a third-party screenshot.<\/p>\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ agents<\/p><\/td>\n

Split<\/p><\/td>\n

Flash: complete guard plus print, cheaper loops. Scout: image in the loop, slightly larger window.<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

DeepSeek V4 Flash<\/p><\/td>\n

Complete letter, no preamble. Scout warmer and unfinished in our capture.<\/p><\/td>\n<\/tr>\n

Long docs \/ multimodal<\/p><\/td>\n

Llama 4 Scout<\/p><\/td>\n

1.31M vs 1.05M. Scout lists image. Flash is text-only.<\/p><\/td>\n<\/tr>\n

Realtime \/ conversational<\/p><\/td>\n

Product-dependent<\/p><\/td>\n

Flash is positioned as fast inference. Not measured here. Confirm tools in your app.<\/p><\/td>\n<\/tr>\n

Cost at volume<\/p><\/td>\n

DeepSeek V4 Flash<\/p><\/td>\n

Cheaper on chat, repo, and the cached agent recipe.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\nHow to read benchmarks<\/strong>\n

Leaderboards mix harnesses, tool settings, and effort modes. When we do not have a primary table for a pair, we do not invent one. Re-test on your workload.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (i10X pack, 2026-08-26)<\/h2>\n

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, and false premise only. No invented extra scores.<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

Llama 4 Scout (excerpt, sanitized):<\/strong> Meta opener, then Hi, hope you’re doing well, Q3 deck last Tuesday, finance still missing after Friday, Wednesday stakeholder ask. Capture cut at the competitive slide.<\/p>\n

DeepSeek V4 Flash (excerpt, sanitized):<\/strong> \u201cHi there,\u201d same Tuesday \/ Friday facts, Wednesday ask, complete Acme pricing line, \u201cThanks so much!\u201d<\/p>\n

Edge:<\/strong> Flash for a finished internal note. Scout for extra warmth if you strip the preamble and wait for Acme.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

Both named ZeroDivisionError<\/code>. Flash proposed if not nums: return 0<\/code> and printed average([])<\/code>. Scout returned None<\/code> and truncated. Flash<\/strong> on snippet completeness.<\/p>\n\n

Test 3: False premise (Moon cheese)<\/h3>\n

Both refused first. Scout stopped at rock and inorganic compounds. Flash refused, then processed cheese curds for casein. Scout<\/strong> on trust-stop. Flash still named the premise as false.<\/p>\n\n\n\n\n\n\n\n\n

Prompt type<\/p><\/th>\n

Llama 4 Scout<\/p><\/th>\n

DeepSeek V4 Flash<\/p><\/th>\n

Note<\/p><\/th>\n<\/tr>\n

Client email rewrite<\/p><\/td>\n

20\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Flash complete. Scout preamble plus truncation.<\/p><\/td>\n<\/tr>\n

Bug explain + minimal fix<\/p><\/td>\n

21\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Both catch ZeroDivisionError. Flash\u2019s guard plus print is complete.<\/p><\/td>\n<\/tr>\n

Logic + false premise<\/p><\/td>\n

24\/25<\/p><\/td>\n

20\/25<\/p><\/td>\n

Both refuse. Scout stops. Flash continues a cheese plan.<\/p><\/td>\n<\/tr>\n

Total<\/strong><\/p><\/td>\n

65\/75<\/strong><\/p><\/td>\n

67\/75<\/strong><\/p><\/td>\n

Flash ahead on this pack. Scout still owns image and the stricter stop.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A 2-point quality gap does not erase Scout\u2019s image card. It also does not erase Flash\u2019s cheaper meter. Route the next step.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n