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Data

Data as of October 6, 2026 · How the score is built

CurrentWeight access pendingHybrid

Released Oct 6, 20261M context

Mistral Large 4

Decision readingMistral Large 4 scores 53.7 out of 100 and ranks #69 of 214. This profile shows 3 source-displayable benchmark rows. API pricing is $0.68 input and $2.09 output per million tokens, with cached input at $0.07.

Released Oct 6, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

53.7/100

field median 52#69 of 214 ranked models

Public

#69of 214

Verified —

Price

$0.68input / $2.09 output

input median $0.95cached $0.070 · blended $1.39

Speed

116tok/s

field median 98 tok/sFirst token 18.69 s

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Published rows are visible, but no category has enough eligible evidence for a comparative rank.

Validate before choosing

3 published rows leave some tracked benchmark slots empty.

Source-linked · 3 displayable benchmark rows

Follow model changes

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticWeight 22%2 benchmarksVerified
Score pending
CodingWeight 20%1 benchmarkVerified
Score pending
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeWeight 12%0 benchmarksNot measured
Not measured
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathWeight 5%0 benchmarksNot measured
Not measured

3 of 655 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic2/2 verified
  2. Coding1/1 verified
  3. ReasoningNot measured
  4. MultimodalNot measured
  5. KnowledgeNot measured
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. MathNot measured
Verified sourceProvisionalNot measured

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding1 row
Coding benchmark values, best verified comparison, weight, and source status
Vibe Code BenchVibe Code Bench v1.1Score78.40%Versus best verified row

Best verified: Gemini 4 Argon · 91.90%

Gap13.5 behindWeightDisplay only
Agentic2 rows
Agentic benchmark values, best verified comparison, weight, and source status
CybenchScore93.0%Versus best verified row

Best verified: Mistral Large 4 · 93.0%

GapBest verifiedWeightDisplay only
Finance Agent v2Score54.7%Versus best verified row

Best verified: Gemini 4 Argon · 65.4%

Gap10.7 behindWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 3 rows

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Current modelExplore all models

Mistral Large 4 · 53.7 score · $1.39 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

30405060708090100$0.10$0.50$1$5$10$25$50$100↘ frontierMistral Large 4

Horizontal: blended price per million tokens, log scale · Vertical: public score

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Dec 2, 2025

    Mistral Large 3

    Not publicly ranked · $0.5 / $1.5

  2. Oct 6, 2026 · you are here

    Mistral Large 4

    Score 53.7 · $0.68 / $2.09

Base entry

Radar

Mistral Large 4 release history

Full release history

Radar confirmed these at the source. Use Mistral Large 4 in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Public preview through Mistral Studio; Mistral says the weights are due by the end of October 2026.
Cloud regions
Not tracked yet
Lifecycle
Current
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Published at $0.070 per million cached input tokensMistral AI Mistral Large 4 model documentation
Self-host
Weight availability is pending
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

Mistral Large 4 ranks #69 of 214 on the public leaderboard with a score of 53.69/100. It does not yet have enough sourced coverage for a verified position.

Mistral Large 4 has no verified publication yet for weight access. Its 1M context window is documented separately from that pending weight status. The profile records its reasoning mode as hybrid.

Public preview through Mistral Studio; Mistral says the weights are due by the end of October 2026.

Mistral announced a public preview on October 6, 2026, and scheduled the weights for month-end. Its post reports 93% on Cybench. The refreshed Vals boards report 78.403 on Vibe Code Bench v1.1 and 54.678 on Finance Agent v2. Those display results do not enter the weighted ranking basket.

Its explicit predecessor is Mistral Large 3. 3 of 655 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Last updated October 6, 2026. Runtime fields remain blank until a sourced snapshot exists.

Questions

How does Mistral Large 4 perform overall in AI benchmarks?

Mistral Large 4 ranks #69 out of 214 models on the public BenchAlign leaderboard, with a score of 53.69/100. Its evidence status is Estimated, and this profile shows 3 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is Mistral Large 4 good for coding and programming?

Mistral Large 4 has source-displayable benchmark coverage for coding and programming, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Mistral Large 4 good for agentic tool use and computer tasks?

Mistral Large 4 has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Does Mistral Large 4 have full benchmark coverage on BenchLM?

No. Mistral Large 4 currently has 18 source-displayable rows across 655 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of Mistral Large 4?

Mistral Large 4 has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

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