Mistral Medium 3.5 pricing: real cost per million tokens

What Mistral's Mistral Medium 3.5 really costs across 1 plan from 1 provider: monthly fees, usable allowances and real per-token prices, against the official API list price and independent leaderboards.

Data updated · How real prices are computed

As of 2026-10-08, the cheapest way to use Mistral Medium 3.5 is Droid Max at a real cost of about $0.02969 per million tokens ($200/month for roughly 6.736B tokens a month). We track 1 plan and API channels that serve Mistral Medium 3.5.

Lowest real price
$0.02969/MTokDroid Max
API list price
—
Most monthly tokens
6.736BDroid Max
Plans & APIs
11 subscription · 0 metered APIs
Maker
Mistral
Model ID
mistral-medium-3.5
Providers
Factory
Leaderboards
Artificial Analysis Intelligence Index v4.3, Terminal-Bench 4.0 (AA), Code Arena | WebDev Overall, Agent Arena · Overall (Ranking / Pareto)

Plot Mistral Medium 3.5 on the price–capability chart →

Mistral Medium 3.5 plans and real prices

Real price = monthly fee ÷ tokens you can actually use in a month. “vs API” shows how many times cheaper than the official API list price (standard workload mix) the plan is. “Evidence” opens the full calculation and sources for that row.

Every tracked way to use Mistral Medium 3.5, cheapest first
PlanProviderBillingMonthly feeMonthly tokensReal $/MTokvs APIConfidenceData date
Droid MaxFactorySubscription$200/mo6.736B$0.02969—MediumEvidence ↓

Behind each number

For every plan: the data date, the exact calculation, the quota basis, how leaderboard scores were mapped, and the original adoption evidence with its sources.

Droid MaxFactory$0.0296912/MTok
Data date
2026-09-29 · derived; uses the data date of Droid Max · claude-opus-5.5
Real price / MTok
$0.0296912
Monthly tokens
6,736,000,000 (6.736B)
Quota confidence
Medium
Calculation
$200 / month ÷ 6,736,000,000 tokens × 1,000,000 ≈ $0.0296912 / MTok
Quota basis

Anthropic workload: 97% cache reads / 2.5% cache writes / 0.5% output

Benchmark mapping
Artificial Analysis Intelligence Index v4.3: Medium (1 configuration)
Terminal-Bench 4.0 (AA): Low (1 configuration)
Code Arena | WebDev Overall: Low (1 configuration)
Agent Arena · Overall (Ranking / Pareto): Low (1 configuration)Mapping confidence: how directly each score refers to the model served here.

Adoption evidence · source and rationale (translated from the Chinese record)

Derived from same-plan claude-opus-5.5 2.526B × 2.666666666666667

Factory official multipliers Opus 5.5 1.6× / mistral-medium-3.5 0.6×: the same Standard Usage pool scaled by multiplier — not a measurement of this model; inherits the Opus 5.5 row's Anthropic-tier workload, not recomputed for each model's own price gap; docs.factory.ai/docs/models; droid-model-multipliers-2026-09-29.json; Droid Core models draw Standard Usage first, then a separate free open-source pool applies (independent cap unmeasured); this value counts only the Standard Usage part

Mistral Medium 3.5 benchmark scores

Highest archived configuration per leaderboard; ranks are among all models each board archives in this dataset. Scores are never mixed across boards.

Mistral Medium 3.5 on independent leaderboards
LeaderboardMetricScoreRankConfigurationEffortHarnessConfigsSnapshot
Artificial Analysis Intelligence Index v4.3Intelligence Index14.19#60 / 60Mistral Medium 3.5Medium—12026-10-01
Terminal-Bench 4.0 (AA)Resolution Rate %0#49 / 53Artificial Analysis - Mistral Medium 3.5MediumArtificial Analysis12026-10-01
Code Arena | WebDev OverallArena Score1,263#54 / 54mistral-medium-3.5Medium—12026-09-30
Agent Arena · Overall (Ranking / Pareto)Net Improvement %-11.74#39 / 39Mistral Medium 3.5Medium—12026-09-30

Every archived configuration

Artificial Analysis Intelligence Index v4.3 1 configuration
Artificial Analysis Intelligence Index v4.3: every archived configuration (Intelligence Index)
ConfigurationScoreScore intervalHarness / effort / modeSource task cost (mean / median)CheckedLinks
Mistral Medium 3.514.1889—— / Medium / —— / —2026-10-01Source · Archive
Terminal-Bench 4.0 (AA) 1 configuration
Terminal-Bench 4.0 (AA): every archived configuration (Resolution Rate %)
ConfigurationScoreScore intervalHarness / effort / modeSource task cost (mean / median)CheckedLinks
Artificial Analysis - Mistral Medium 3.5AA's own-harness independent measurement of Terminal-Bench 4.0 (the terminalBench40 field in the models-board payload, fraction×100); scored separately from the official tbench.ai board.0—Artificial Analysis / Medium / —— / —2026-10-01Source · Archive
Code Arena | WebDev Overall 1 configuration
Code Arena | WebDev Overall: every archived configuration (Arena Score)
ConfigurationScoreScore intervalHarness / effort / modeSource task cost (mean / median)CheckedLinks
mistral-medium-3.51,2631,248.2 – 1,277.8— / Medium / —— / —2026-10-01Source · Archive
Agent Arena · Overall (Ranking / Pareto) 1 configuration
Agent Arena · Overall (Ranking / Pareto): every archived configuration (Net Improvement %)
ConfigurationScoreScore intervalHarness / effort / modeSource task cost (mean / median)CheckedLinks
Mistral Medium 3.5-11.74-13.24 – -10.24— / Medium / —— / $0.672026-10-01Source · Archive

These are reference mappings, not benchmarks of a subscription or API channel. Harness and quota-measurement effort alignment are unverified. Source task costs are not subscription task costs.

Frequently asked questions

How much does Mistral Medium 3.5 really cost per million tokens?

Measured as monthly fee ÷ tokens you can actually use, Mistral Medium 3.5 ranges from $0.02969 (Droid Max) to $0.02969 (Droid Max) per million tokens.

Which subscription gives the most Mistral Medium 3.5 usage?

Droid Max allows roughly 6.736B tokens of Mistral Medium 3.5 per month for $200.

How does Mistral Medium 3.5 score on independent leaderboards?

Mistral Medium 3.5 has scores on 4 leaderboards; its strongest placement is 0 (Resolution Rate %) on Terminal-Bench 4.0 (AA), #49 of 53 models tracked.

Who makes Mistral Medium 3.5, and where can I use it?

Mistral Medium 3.5 (model ID mistral-medium-3.5) is made by Mistral and is available through Factory.