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Mistral Nemo vs Qwen2.5 72B Instruct

Mistral Nemo

Mistral AI

40#358
vs
Signal-by-Signal Comparison
SignalMistral NemoDeltaQwen2.5 72B Instruct
Capabilities
50
--
50
Pricing
100
+0
100
Context window size
81
+10
72
Recency
0
-7
8
Output Capacity
70
--
70
Overall Result
2 wins
of 5
1 wins
Mistral Nemo wins 2 of 5 signals

Score History

Score History (25 data points)
Mistral NemoQwen2.5 72B Instruct
Mistral Nemo

40

current score

Leader

Tied

right now

Qwen2.5 72B Instruct

40

current score

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

Mistral Nemo

Mistral AI

Best Value
Per request$0.000034
Daily$0.11
Monthly$3.40
Annual$40.80

Qwen2.5 72B Instruct

Alibaba

Per request$0.000560
Daily$1.87
Monthly$56.00
Annual$672.00

Mistral Nemo saves you $52.60/month

That's $631.20/year compared to Qwen2.5 72B Instruct at your current usage level of 100K calls/month.

94% cheaper
Choose Mistral Nemo for cost optimization

Mistral Nemo pricing:
Input:$0.02/M tokens
Output:$0.03/M tokens
Qwen2.5 72B Instruct pricing:
Input:$0.36/M tokens
Output:$0.40/M tokens
Tie
Mistral Nemo

Mistral AI

40

Composite Score

Tie
Qwen2.5 72B Instruct

Alibaba

40

Composite Score

Signal-by-Signal Comparison
MetricMistral NemoQwen2.5 72B InstructWinner
Overall Score
40
40
--
Rank#358#357
Qwen2.5 72B Instruct
Quality Rank#358#357
Qwen2.5 72B Instruct
Adoption Rank#358#357
Qwen2.5 72B Instruct
Parameters--72B--
Context Window131K33K
Mistral Nemo
Pricing$0.02/$0.03/M$0.36/$0.40/M--
Signal Scores
Capabilities
50
50
Mistral Nemo
Pricing
100
100
Mistral Nemo
Context window size
81
72
Mistral Nemo
Recency
0
8
Qwen2.5 72B Instruct
Output Capacity
70
70
Mistral Nemo
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 NemoEntry Level

Scores 40/100 (rank #358), placing it in the top -23% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Qwen2.5 72B InstructEntry Level

Scores 40/100 (rank #357), placing it in the top -23% 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 Nemo when you need:

  • High-volume production workloads where API costs must be minimized
  • Processing long documents or large codebases (131K token context)
  • Self-hosted deployments where you need full control over the model

Choose Qwen2.5 72B Instruct when you need:

  • Self-hosted deployments where you need full control over the model
Cost-Performance Analysis
Mistral NemoBest Value
Input cost$0.02/M tokens
Output cost$0.03/M tokens
Cost per quality point$0.001
Est. monthly (1M tokens/day)$0.73
Qwen2.5 72B Instruct
Input cost$0.36/M tokens
Output cost$0.40/M tokens
Cost per quality point$0.019
Est. monthly (1M tokens/day)$11.40

Mistral Nemo offers 94% better value per quality point. At 1M tokens/day, you'd spend $0.73/month with Mistral Nemo vs $11.40/month with Qwen2.5 72B Instruct - a $10.66 monthly difference.

Latency & Speed
Mistral NemoFaster
Speed score0/100
Qwen2.5 72B Instruct
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 Nemo

Customer support chatbot

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

Mistral Nemo

Long document analysis

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

Mistral Nemo

Batch data extraction

Lower output pricing ($0.03/M) reduces costs when processing thousands of records daily

Mistral Nemo

Creative writing & content

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

Mistral Nemo
Which Should You Choose?
Our recommendation:
Mistral Nemo

Mistral Nemo and Qwen2.5 72B Instruct 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.

Mistral Nemo
Recommended

by Mistral AI

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Cost - 94% 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 Alibaba

Consider for specialized use cases.

Capability Comparison
CapabilityMistral NemoQwen2.5 72B Instruct
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 Nemo

Mistral AI

Best Value
$0.0702
estimated monthly cost

Qwen2.5 72B Instruct

Alibaba

$1.13
estimated monthly cost

Mistral Nemo saves you $1.06/month

That's 94% cheaper than Qwen2.5 72B Instruct at 1,000 tokens/request and 100 requests/day.

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

Parameters & Context
ParameterMistral NemoQwen2.5 72B Instruct
Context Window131K33K
Max Output Tokens16,38416,384
Open SourceYesYes
CreatedJul 19, 2024Sep 19, 2024
Last updated: 35m ago

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