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Mistral Medium 3.1 vs Mistral Medium 3.1 (batch)

Mistral Medium 3.1

Mistral AI

40#311
vs
Signal-by-Signal Comparison
SignalMistral Medium 3.1DeltaMistral Medium 3.1 (batch)
Capabilities
67
--
67
Pricing
98
--
98
Context window size
81
--
81
Recency
64
--
64
Output Capacity
80
--
80
Overall Result
0 wins
of 5
0 wins
It's a tie - both models win 0 signals each

Score History

Score History (28 data points)
Mistral Medium 3.1Mistral Medium 3.1 (batch)
Mistral Medium 3.1

40

current score

Leader

Tied

right now

Mistral Medium 3.1 (batch)

40

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

Mistral Medium 3.1

Mistral AI

Per request$0.001400
Daily$4.67
Monthly$140.00
Annual$1680.00

Mistral Medium 3.1 (batch)

Mistral AI

Per request$0.001400
Daily$4.67
Monthly$140.00
Annual$1680.00
Mistral Medium 3.1 pricing:
Input:$0.40/M tokens
Output:$2.00/M tokens
Mistral Medium 3.1 (batch) pricing:
Input:$0.40/M tokens
Output:$2.00/M tokens
Tie
Mistral Medium 3.1

Mistral AI

40

Composite Score

Tie
Mistral Medium 3.1 (batch)

Mistral AI

40

Composite Score

Signal-by-Signal Comparison
MetricMistral Medium 3.1Mistral Medium 3.1 (batch)Winner
Overall Score
40
40
--
Rank#311#312
Mistral Medium 3.1
Quality Rank#311#312
Mistral Medium 3.1
Adoption Rank#311#312
Mistral Medium 3.1
Parameters------
Context Window131K131K--
Pricing$0.40/$2.00/M$0.40/$2.00/M--
Signal Scores
Capabilities
67
67
Mistral Medium 3.1
Pricing
98
98
Mistral Medium 3.1
Context window size
81
81
Mistral Medium 3.1
Recency
64
64
Mistral Medium 3.1
Output Capacity
80
80
Mistral Medium 3.1
Benchmark Head-to-Head(1 benchmarks)
Mistral Medium: 01 tiesMistral Medium: 0
Mistral Medium
Mistral Medium
Normalized 0-100%
HLE
4.52%4.52%
Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

Mistral Medium 3.1Entry Level

Scores 40/100 (rank #311), placing it in the top -7% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Mistral Medium 3.1 (batch)Entry Level

Scores 40/100 (rank #312), placing it in the top -7% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.

When to Use Each Model

Choose Mistral Medium 3.1 when you need:

  • Budget-friendly applications with moderate quality requirements

Choose Mistral Medium 3.1 (batch) when you need:

  • Budget-friendly applications with moderate quality requirements
Cost-Performance Analysis
Mistral Medium 3.1Best Value
Input cost$0.40/M tokens
Output cost$2.00/M tokens
Cost per quality point$0.060
Est. monthly (1M tokens/day)$36.00
Mistral Medium 3.1 (batch)
Input cost$0.40/M tokens
Output cost$2.00/M tokens
Cost per quality point$0.060
Est. monthly (1M tokens/day)$36.00

Both models are priced similarly, so the decision comes down to quality and features rather than cost.

Latency & Speed
Mistral Medium 3.1Faster
Speed score0/100
Mistral Medium 3.1 (batch)
Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

Mistral Medium 3.1

Customer support chatbot

Suitable for user-facing chat with competitive response times. Mistral Medium 3.1 also offers lower per-token costs for high-volume support

Mistral Medium 3.1

Long document analysis

Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass

Mistral Medium 3.1

Batch data extraction

Lower output pricing ($2.00/M) reduces costs when processing thousands of records daily

Mistral Medium 3.1

Creative writing & content

Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content

Mistral Medium 3.1

Image understanding & OCR

Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly

Mistral Medium 3.1
Which Should You Choose?
Our recommendation:
Mistral Medium 3.1

Mistral Medium 3.1 and Mistral Medium 3.1 (batch) are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

by Mistral AI

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Cost - 0% lower pricing; better value at scale
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale

by Mistral AI

Consider for specialized use cases.

Capability Comparison
CapabilityMistral Medium 3.1Mistral Medium 3.1 (batch)
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoning
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

Mistral Medium 3.1

Mistral AI

$3.12
estimated monthly cost

Mistral Medium 3.1 (batch)

Mistral AI

$3.12
estimated monthly cost

Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.

Parameters & Context
ParameterMistral Medium 3.1Mistral Medium 3.1 (batch)
Context Window131K131K
Max Output Tokens104,857104,857
Open SourceNoNo
CreatedAug 13, 2025Aug 13, 2025
Last updated: 2m ago

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