Mistral Large 3 vs Hy4 preview

At a Glance

Compare
Mistral Large 3Mistral AI
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v4.4#28 of 5743.0 score · 1/3 sources · single-source provisional · missing LiveBench, ARC-AGI-2 · full-core range 14.3–81.0UnrankedNot in the 57-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.25Openrouter ↗ · Oct 19, 2026$0.834Deepinfra ↗ · Oct 19, 2026
Output priceFrom · USD / 1M tokens$0.75Openrouter ↗ · Oct 19, 2026$2.501Deepinfra ↗ · Oct 19, 2026
Context windowMaximum documented tokens262K1,000K
Model facts checkedAug 29, 2026View model evidence →Sep 2, 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

FieldMistral Large 3Hy4 preview
DeveloperMistral AITencent
FamilyMistral Large 3Hy4
ModelMistral Large 3Hy4 preview
VersionMistral Large 3Hy4 preview
Lifecycleactivepreview
Released2025-12-022026-08-28
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, DocumentText
Output modalitiesTextText
Context window262K1,000K
Total parameters675B770B
Active parameters41B49B
LicenseApache-2.0apache-2.0
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessMistral AI (Standard), Openrouter (Standard)Deepinfra (Standard), Openrouter (Standard)
Capabilitiesagents, chat, generation, structured_outputs, tools, visionchat, generation, reasoning, tools

Mistral Large 3 Capabilities

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

Hy4 preview Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDtencent/Hy4-preview

Primary Evidence

Sources and Freshness

Questions

Mistral Large 3 vs Hy4 preview FAQs

Is Mistral Large 3 or Hy4 preview better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Mistral Large 3 and Hy4 preview, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Mistral Large 3 or Hy4 preview?+

Mistral Large 3 is $0.25 and Hy4 preview is $0.834 per million tokens, so Mistral Large 3 is cheaper on this metric. Mistral Large 3 is $0.75 and Hy4 preview is $2.501 per million tokens, so Mistral Large 3 is cheaper on this metric.

Which has a larger context window, Mistral Large 3 or Hy4 preview?+

Hy4 preview has the larger sourced context window. Mistral Large 3 supports 262,144 tokens and Hy4 preview supports 1,000,000 tokens.

Which performs better in benchmarks, Mistral Large 3 or Hy4 preview?+

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

Can Mistral Large 3 or Hy4 preview be self-hosted?+

Both models have the same sourced self-hosting status: supported. Mistral Large 3 is open weight; Hy4 preview is open weight.

Can Mistral Large 3 and Hy4 preview understand images?+

Mistral Large 3 is documented with image input; Hy4 preview is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Mistral Large 3 or Hy4 preview?+

Neither model has a sourced maximum output in this comparison, so a larger limit cannot be determined.

Do Mistral Large 3 and Hy4 preview support reasoning and tool use?+

Mistral Large 3: tool calling and image input. Hy4 preview: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Mistral Large 3 or Hy4 preview?+

Mistral Large 3 has 2 sourced provider routes; Hy4 preview has 2, a tie.

Which offers better value, Mistral Large 3 or Hy4 preview?+

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.

Send Feedback