Llama 4 Scout 17B 16E vs Mistral Large 3

At a Glance

Compare
Mistral Large 3Mistral AI
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.25Openrouter · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$0.75Openrouter · Sep 22, 2026
Context windowMaximum documented tokens10,000K262K
Model facts checkedAug 28, 2026View model evidence →Aug 29, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldLlama-4-Scout-17B-16EMistral Large 3
DeveloperMetaMistral AI
FamilyLlama 4 Scout 17b 16eMistral Large 3
ModelLlama-4-Scout-17B-16EMistral Large 3
VersionLlama-4-Scout-17B-16EMistral Large 3
Lifecycleactiveactive
Released2025-04-052025-12-02
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image, Document
Output modalitiesTextText
Context window10,000K262K
Total parameters108.6B675B
Active parameters17B41B
LicenseotherApache-2.0
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessTogether Ai (Standard)Mistral AI (Standard), Openrouter (Standard)
Capabilitieschat, generation, toolsagents, chat, generation, structured_outputs, tools, vision

Llama 4 Scout 17B 16E Capabilities

chatgenerationtools
Serving providers1
Canonical IDmeta-llama/Llama-4-Scout-17B-16E

Mistral Large 3 Capabilities

agentschatgenerationstructured outputstoolsvision
Serving providers2
Canonical IDmistralai/mistral-large-2512

Primary Evidence

Sources and Freshness

Questions

Llama 4 Scout 17B 16E vs Mistral Large 3 FAQs

Is Llama 4 Scout 17B 16E or Mistral Large 3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E and Mistral Large 3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Llama 4 Scout 17B 16E or Mistral Large 3?+

Only Mistral Large 3 has a directly sourced input price: $0.25 per million tokens. Only Mistral Large 3 has a directly sourced output price: $0.75 per million tokens.

Which has a larger context window, Llama 4 Scout 17B 16E or Mistral Large 3?+

Llama 4 Scout 17B 16E has the larger sourced context window. Llama 4 Scout 17B 16E supports 10,000K and Mistral Large 3 supports 262K.

Which performs better in benchmarks, Llama 4 Scout 17B 16E or Mistral Large 3?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Llama 4 Scout 17B 16E or Mistral Large 3 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E is open weight; Mistral Large 3 is open weight.

Can Llama 4 Scout 17B 16E and Mistral Large 3 understand images?+

Llama 4 Scout 17B 16E is documented with image input; Mistral Large 3 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Llama 4 Scout 17B 16E or Mistral Large 3?+

Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E is — and Mistral Large 3 is —.

Do Llama 4 Scout 17B 16E and Mistral Large 3 support reasoning and tool use?+

Llama 4 Scout 17B 16E: tool calling and image input. Mistral Large 3: tool calling and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Llama 4 Scout 17B 16E or Mistral Large 3?+

Llama 4 Scout 17B 16E has 1 sourced provider route; Mistral Large 3 has 2, so Mistral Large 3 has broader tracked availability.

Which offers better value, Llama 4 Scout 17B 16E or Mistral Large 3?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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