Mistral Medium 3.1

retired

by Mistral AI · Mistral Medium family · best for cheap multilingual multimodal chat at volume

Cost-OptimizedMultimodalmultilingual
7.7
AI Panel Score
Value 9.0/10

Mistral deprecated Mistral Medium 3.1 on 2026-05-22 with a retirement date of 2026-08-31 and names Mistral Medium 3.5 as its replacement; the model is gone from Mistral's API pricing page, the mistral-medium-3 alias now points to Medium 3.5, and as a closed-weight model it has no self-host path. Mistral Medium 3.1 (release 25.08, shipped 13 August 2025) was the cost-optimised workhorse of Mistral's proprietary "Premier" tier: a closed-weight, multimodal, 128K-context model priced at $0.40/$2.00 per 1M tokens — among the cheapest frontier-adjacent multimodal models on the market. It is an incremental refinement of Medium 3 (May 2025) with better instruction following, sharper tool use, and improved vision. The buyer's sentence: when budget was the dominant constraint and the workload chat/Q&A rather than agentic coding, this was the value pick until its retirement; Medium 3.5 is the replacement.

What's new

  • Incremental update over Medium 3 (May 2025): better instruction following, sharper tool use, improved vision.
  • Retains Medium 3's pricing — one of the cheapest frontier-adjacent multimodal models available.
  • Remains Premier tier (proprietary, closed weights); open weights only arrived in this family with Medium 3.5 in April 2026.
  • No configurable reasoning effort — for extended thinking, callers escalate to Magistral or Medium 3.5.

Benchmarks

BenchmarkScoreSource
HumanEval92%artificialanalysis.ai 2026-05-28T00:00:00.000Z
LMArena Elo1409lmarena.ai 2026-09-13T00:00:00.000Z
GPQA Diamond57%artificialanalysis.ai 2026-05-28T00:00:00.000Z
LMArena Coding Elo1454lmarena.ai 2026-09-13T00:00:00.000Z

AI Panel Review

Six personas, six verdicts — the same panel that reviews every product on TopReviewed.

Decision Maker7.5/10
“The model I deployed from late 2025 until its August 2026 retirement — cheap, multilingual, multimodal, stable — before Medium 3.5 replaced it.”

Medium 3.1 has been the dependable production workhorse: inexpensive, multilingual, multimodal, and operationally reliable on La Plateforme. The strategic weakness is closed weights — no on-prem, so the sovereignty story is limited to EU-hosted API. Medium 3.5 offers open weights and a reasoning knob, and Mistral retired 3.1 on 2026-08-31 (deprecated 2026-05-22) with Medium 3.5 as the replacement, so the chat surface it served for mid-sized European SaaS has to move to Medium 3.5.

Strategic Fit 7Vendor Risk 7Roadmap Confidence 8
Pros
  • cheap, stable, multimodal, multilingual
Cons
  • closed weights
  • superseded for agents
Right for: cost-bound chat/Q&A until the 2026-08-31 retirement
Avoid if: you need it after its 2026-08-31 retirement, self-host, or agentic coding
Domain Strategist7/10
“It owns the 'cheap capable multimodal' slot in Europe, but its successor is already eating its strategic mindshare.”

Medium 3.1's positioning was sharp at launch: frontier-adjacent multimodal capability at a price that unlocked high-volume European-language use cases US flagships couldn't touch economically. That slot is still valuable, but the strategic narrative has moved to Medium 3.5 (open weights, merged architecture). 3.1 now reads as the mature, boring, cost-tier option rather than the headline. Against GPT-5 mini and Claude Haiku it competes on EU-language quality and price; against its own successor it competed only on cost. It held the slot as an incumbent until Mistral retired it on 2026-08-31.

Competitive Positioning 7Differentiation 6Market Timing 6
Pros
  • proven cost-tier slot, EU-language edge
Cons
  • outshone by its own successor
Right for: cost-led European deployments
Avoid if: you want the current flagship narrative
Finance Lead9/10
“$0.40 in, $2.00 out made the high-volume bill a fraction of GPT-5 or Sonnet — until the 2026-08-31 retirement forced a move to Medium 3.5.”

Pricing was the entire story. $0.40/$2.00 makes Medium 3.1 a standout value among multimodal models with frontier-adjacent quality. For high-volume support tickets, summarisation, classification, and content variants, the monthly bill is a fraction of US flagships. The $0.04 cached-input rate and ~50% batch discount sharpen it further, and predictable throughput makes forecasts simple. There is no self-host lever (closed weights), so this is a pure API-economics play — but at this price, for the right workload, the unit economics were excellent. Mistral retired it on 2026-08-31, so budgets now have to absorb Medium 3.5's $1.50/$7.50.

Cost Efficiency 9Pricing Transparency 9Value per Dollar 9
Pros
  • very low price, cache/batch discounts, predictable
Cons
  • no self-host capex option
Right for: high-volume API chat until the retirement
Avoid if: you need it past its 2026-08-31 retirement, or wanted to amortise GPUs
Domain Practitioner7.5/10
“The most boring-in-a-good-way Mistral model I've shipped — clean API, predictable JSON, reliable vision parsing.”

After extended production use, Medium 3.1 is the dependable one: clean OpenAI-compatible API, predictable JSON tool use, reliable vision-input document parsing. No reasoning toggle, so for hard problems I route to Magistral or 3.5. Lower output throughput is occasionally annoying for streaming UX but fine for backend pipelines. It was the "good enough at minimum cost" default for builders who don't need agentic firepower, until its 2026-08-31 retirement. Docs are thinner than Anthropic's but adequate.

API Ergonomics 8Tool/Agent Support 7Reliability 8
Pros
  • stable, predictable, reliable vision
Cons
  • no reasoning toggle
  • slow streaming
Right for: backend chat/extraction pipelines
Avoid if: you need agentic tool depth
Power User7/10
“Responsive and helpful for everyday questions; for routine help I can't tell it isn't a flagship — except the stream is a touch slower.”

In Le Chat at the Medium tier, Medium 3.1 is responsive and helpful for everyday questions, with excellent European-language quality. It feels slightly less polished than Large 3 or top US models on nuanced creative tasks, but for routine help the difference is hard to notice. Throughput is the main felt downside — replies stream a touch slower than competitors. Refusal rate is moderate and reasonable. A capable, unremarkable daily driver.

Output Quality 7Speed 6.5Everyday Usefulness 7.5
Pros
  • helpful, strong EU languages
Cons
  • slower streaming
  • not the most polished
Right for: routine everyday help
Avoid if: you want flagship polish or speed
Skeptic7/10
“Cheap and competent, but it's a closed model with thin published benchmarks and a successor that already beats it — buy it for price, nothing else.”

There is little to over-claim here, which makes the skeptic's job easy. Medium 3.1 is honestly positioned as a cost-tier model and delivers on that. The caveats: it is closed-weight, so the "EU sovereignty" halo around Mistral doesn't fully apply (no on-prem); Mistral published almost no numeric benchmarks, so the quality picture leans on third-party aggregation; and its own successor outperforms it on agentic/coding. None of this is deceptive — just a reminder that the value is price-per-token and not capability leadership. It was the pick where the bill dominated the decision, until the 2026-08-31 retirement.

Claim Accuracy 8Weakness Severity 6Hype vs Reality 7
Pros
  • honestly cheap, no inflated claims
Cons
  • closed
  • thin benchmarks
  • superseded
Right for: cost-driven workloads
Avoid if: you need sovereignty-by-on-prem or top capability

Strengths

  • Best $/intelligence ratio among Premier-tier multimodal models in its window.
  • Frontier-adjacent non-reasoning quality at a fraction of flagship cost.
  • Strong multilingual European-language performance.
  • Mature, stable API after extended production usage.
  • Native vision at a low price point.

Limitations

  • Closed weights (Premier) — no self-host, unlike Medium 3.5.
  • Output throughput is on the slow side of the price tier.
  • No native extended reasoning — separate model call required.
  • Superseded by Medium 3.5, Mistral's named replacement, and retired on 2026-08-31 (deprecated 2026-05-22).
  • Sparse public benchmark coverage from Mistral itself.

Best use cases

These were its best uses until the 2026-08-31 retirement; Mistral Medium 3.5 is the replacement.

  • High-volume customer support and chat where cost dominates.
  • Multilingual content workflows in European languages.
  • Vision-input apps (receipts, charts, screenshots) at a fraction of GPT-class pricing.
  • Workloads needing fast, cheap, capable chat — not reasoning or agentic coding.

Deep dive

The full research notes behind this review — verified against primary sources.

Architecture

Medium 3.1 is a dense transformer, but as a Premier/closed-weight model Mistral does not disclose parameter count, layer count, attention mechanism, or training scale — an honest contrast to the open Mistral models, where these are published. Context is 131K. Tokenizer is mistral_common. Architecture detail is deliberately undisclosed; we record what is verifiable (dense, 131K, multimodal) and null everything Mistral withholds rather than estimate.

Capabilities

Medium 3.1 is a generalist cost-tier model: strong general chat, solid coding (cap_coding 7.0), native vision input, and reliable JSON tool use (cap_function_calling 7.5). The 131K context covers most enterprise documents without paying for 256K (cap_long_context 7.0). Multilingual quality is excellent across European languages (cap_multilingual 8.5). It has no configurable reasoning (cap_reasoning 6.5, cap_math 6.0) — for extended thinking, callers route to Magistral or Medium 3.5. Throughput is on the slow side of its price tier (~45 tps), but the per-token economics more than compensate for batch and asynchronous workloads. No native real-time retrieval (cap_realtime_data 0.0).

Benchmark analysis

Benchmark Score vs Predecessor vs Top Competitor Source
Artificial Analysis Index 21 (May 2026, before AA rescaled its index) +~2 vs Medium 3 above that date's price-tier median (~16) Artificial Analysis
HumanEval ~92% +~1pp parity with Llama 4 Maverick Artificial Analysis
GPQA Diamond ~57% flat trails reasoning models Artificial Analysis

Mistral published little numeric benchmark detail for Medium 3.1; the values above lean on Artificial Analysis aggregation. MMLU-Pro and standardized coding benchmarks are unpublished. Benchmark coverage is partial; confidence in the aggregate scores is medium-to-high but they are secondary-sourced.

Speed & latency

Output throughput is roughly 45 tokens/sec — the slow side of the price tier, which can be felt in streaming chat UX but is a non-issue for backend pipelines and batch jobs. Time-to-first-token is not separately published (null). Medium latency tier overall; the model's appeal is economics, not speed.

Pricing analysis

Surface Cost Notes
API input $0.40 / 1M tok La Plateforme price until retirement (2026-08-31)
API output $2.00 / 1M tok La Plateforme price until retirement
Cached input $0.04 / 1M tok cache read
Batch (in/out) $0.20 / $1.00 ~50% async discount
Direct UI EUR 14.99/mo (~$15) Le Chat Pro
Free tier ~25 msg/day (Le Chat) no card
Cloud Bedrock, Azure AI Foundry, Vertex AI managed

Deployment & access

Medium 3.1 is API-only and closed-weight — no self-host, no Hugging Face weights, in contrast to Medium 3.5's open release. Until its 2026-08-31 retirement it was available on La Plateforme (EU-hosted by default, US options) and managed on Amazon Bedrock, Azure AI Foundry, and Google Vertex AI. For buyers who need self-hosting or sovereignty-by-on-prem, this model does not deliver it; the EU-residency story here is limited to La Plateforme's EU hosting, not on-prem control.

Safety & privacy

Standard Mistral posture: GDPR-native, SOC 2 Type II, ISO 27001/27701, EU AI Act aligned, EU data residency by default, 30-day abuse retention, no training on inputs unless opt-in, Zero Data Retention available. No built-in moderation; separate Mistral Moderation API available. Moderate, consistent refusal calibration.

Ecosystem & tooling

SDKs in Python and TypeScript/JavaScript; integrations with LangChain, LlamaIndex, and Vercel AI SDK. Powered Le Chat and Mistral AI Studio. As a mature, widely-deployed cost-tier model it reached mainstream popularity within Mistral's user base; its successor replaced it on 2026-08-31.

Buyer questions

Can I self-host it?

No — Medium 3.1 is Premier/closed-weight, API-only. For open weights at this tier, use Medium 3.5 (modified-MIT) or step down to Small 4 (Apache 2.0).

Does it reason?

No reasoning toggle. Route hard analytical work to Magistral or Medium 3.5 with high reasoning effort.

Why pick it over Medium 3.5?

You no longer can: Mistral deprecated Medium 3.1 on 2026-05-22 and retired it on 2026-08-31, naming Medium 3.5 as the replacement, and the mistral-medium-3 alias now points to Medium 3.5. Its case was pure cost: $2.00 output vs $7.50.

Is the data EU-resident?

Yes on La Plateforme (EU default), with 30-day abuse retention, no training on inputs unless opt-in, ZDR available — but only as an API, not on-prem.

How fast is it?

~45 tps — adequate for backend, a touch slow for snappy streaming chat.

Which clouds?

Bedrock, Azure AI Foundry, and Vertex AI, plus La Plateforme until its 2026-08-31 retirement.

Comparable models

Mistral Medium 3.5 — Mistral

Its successor and Mistral's named replacement — open weights, reasoning knob, far stronger on agentic coding, but ~3.75x the output price ($7.50 vs $2.00).

Similar price tier, weaker EU-language quality, broader ecosystem and tooling.

Comparable cost tier; weaker multilingual and a different (Anthropic) safety/ecosystem trade.

DeepSeek V3.2 — DeepSeek

Cheaper still, weaker European-language quality, Chinese-origin residency.

Sources

Primary references used to verify this review.

Model specs

Input price
$0.40 / Mtok
Output price
$2 / Mtok
Cached input
$0.04 / Mtok
Batch (in/out)
$0.20 / $1
Context window
131K tokens
Max output
16K tokens
Knowledge cutoff
2025-04
Released
2025-08-13
Modalities
text, image → text
Output speed
~45 tok/s
License
Proprietary
Clouds
Bedrock, Azure AI Foundry, Vertex AI

Does not train on API inputs by default

Other Mistral Medium versions

Last verified 2026-09-21