Mistral Large 3 Compared: How Does It Stack Up Against GPT, Claude, and Gemini?

We're excited to announce that Mistral AI models are now available in AI Crucible. This includes Mistral's flagship model, Mistral Large 3, along with Mistral Medium 3.1, Mistral Small 3.2, and the efficient Ministral edge models.

Mistral AI has rapidly established itself as Europe's leading AI company, building open-weight models that compete directly with closed-source giants. But how does their flagship model actually perform against the established leaders?

This article provides a head-to-head comparison of the four flagship models from major AI providers:

Time to read: 12-15 minutes

Example cost: $0.53 (3 rounds + arbiter analysis)

What Makes Mistral Large 3 Unique?

Mistral Large 3 Architecture

Mistral Large 3 uses a granular Mixture-of-Experts (MoE) architecture with 41B active parameters from a total of 675B parameters. This design achieves strong performance while maintaining reasonable inference costs—a balance that makes it particularly attractive for production deployments.

Key characteristics:

Why This Comparison Matters

Mistral Large 3 enters a competitive market dominated by OpenAI, Anthropic, and Google. For AI Crucible users building ensemble workflows, understanding where Mistral excels—and where it doesn't—helps optimize model selection for different tasks.

Model Specifications at a Glance

Specification Mistral Large 3 GPT-5.1 Claude Opus 4.5 Gemini 3 Pro Preview
Provider Mistral AI OpenAI Anthropic Google
Context Window 256K 128K 200K 2M
Vision Support Yes Yes Yes Yes
Input Cost (per 1M) $0.50 $1.25 $5.00 $2.00
Output Cost (per 1M) $1.50 $10.00 $25.00 $12.00
Architecture MoE (41B/675B) Dense Dense Dense
Latency Class Medium Medium High Medium
Open Weights Yes No No No

Cost analysis: Mistral Large 3 is 2.5x cheaper than GPT-5.1 on input tokens and 10x cheaper than Claude Opus 4.5. However, pricing alone doesn't determine value—quality, speed, and task-specific performance matter equally.

The Comparison Test

We'll run the same complex prompt through all four flagship models and compare:

  1. Response Speed - Total execution time
  2. Token Usage - Input and output token counts
  3. Response Similarity - How much do the models agree?
  4. Output Quality - Comprehensiveness, accuracy, and usefulness

The Test Prompt

We chose a multi-faceted strategic analysis question requiring:

A Series B startup ($15M raised, 45 employees) is building a developer tools platform.
They're currently focused on code review automation but are considering expansion into:
- AI-assisted code generation
- Security vulnerability scanning
- Performance monitoring and optimization

Analyze this strategic decision considering:
1. Market opportunity and competitive landscape for each option
2. Technical complexity and build vs. buy trade-offs
3. Resource allocation and team scaling requirements
4. Risk factors and go-to-market timing
5. Recommended prioritization with rationale

Provide a comprehensive strategic recommendation with specific next steps.

Step 1: Setting Up the Comparison

Navigate to AI Crucible Dashboard

  1. Go to the AI Crucible Dashboard
  2. Click on the prompt input area
  3. Select Competitive Refinement strategy for iterative improvement

Select the Four Flagship Models

From the model selection panel, choose:

Tip: This combination spans European, American, and diverse architectural approaches—ideal for comprehensive analysis.

Configure Settings

Click Run to start the comparison.

Run Comparison

Step 2: Speed Comparison Results

Here's how each model performed on response speed in Round 1:

Round 1 Metrics

Total Execution Time

Model Execution Time Tokens/Second
Gemini 3 Pro Preview 25.82s ~70 tok/s
GPT-5.1 80.80s ~12 tok/s
Claude Opus 4.5 106.43s ~39 tok/s
Mistral Large 3 118.15s ~32 tok/s

Key Observation: Gemini 3 Pro Preview is the clear speed winner, completing in just 25.82 seconds—over 3x faster than the next model. Mistral Large 3 was the slowest at 118.15 seconds, nearly 5x slower than Gemini. The 92-second gap between fastest and slowest is substantial for time-sensitive applications.

Step 3: Token Usage Comparison

Token efficiency affects both cost and response comprehensiveness.

Input Token Processing

All models received the same prompt, but token counting varies by tokenizer:

Model Input Tokens Notes
GPT-5.1 128 Most efficient
Mistral Large 3 129 Very efficient
Gemini 3 Pro Preview 133 Standard encoding
Claude Opus 4.5 141 Slightly verbose

Output Token Generation

Model Output Tokens Total Tokens
GPT-5.1 984 1,112
Gemini 3 Pro Preview 1,803 1,936
Mistral Large 3 3,785 3,914
Claude Opus 4.5 4,096 4,237

Finding: Significant variation in output length. GPT-5.1 produced the most concise response (984 tokens), while Claude Opus 4.5 generated 4x more tokens (4,096). Mistral Large 3 was similarly verbose to Claude, suggesting both models provide more comprehensive, detailed responses.

Total Cost per Response

Model Total Tokens Total Cost
Mistral Large 3 3,914 $0.0057
GPT-5.1 1,112 $0.0100
Gemini 3 Pro Preview 1,936 $0.0219
Claude Opus 4.5 4,237 $0.1031
Combined Total 11,199 $0.1407

Cost Analysis: Mistral Large 3 is the clear cost winner at $0.0057—nearly half the cost of GPT-5.1 despite producing 3.5x more tokens. Claude Opus 4.5 is 18x more expensive than Mistral for this task. At scale:

Volume Mistral Large 3 GPT-5.1 Gemini 3 Pro Preview Claude Opus 4.5
1,000 queries $5.70 $10.00 $21.90 $103.10
10,000 queries $57.00 $100.00 $219.00 $1,031.00
100,000 queries $570.00 $1,000 $2,190 $10,310

Step 4: Response Similarity Analysis

AI Crucible's similarity analysis reveals how much the models agree with each other. Higher similarity suggests convergent thinking; lower similarity indicates diverse perspectives.

Pairwise Similarity Scores

Model Pair Similarity Interpretation
Gemini ↔ GPT 71% High agreement
Claude ↔ Mistral 71% High agreement
Gemini ↔ Mistral 61% Moderate agreement
GPT ↔ Mistral 58% Moderate agreement
Gemini ↔ Claude 51% Low agreement
GPT ↔ Claude 49% Low agreement

Notable pattern: The models form two distinct clusters. Gemini and GPT align closely (71%), as do Claude and Mistral (71%). However, cross-cluster similarity is much lower (49-61%), suggesting fundamentally different reasoning approaches.

Key Overlapping Themes (High Confidence)

All four models agreed on:

  1. AI Code Generation Priority - All recommended this as the highest-priority expansion
  2. Build vs. Buy Assessment - Consensus on building core differentiators, buying commoditized features
  3. Phased Rollout - All suggested sequential rather than parallel expansion
  4. Talent Acquisition - ML/AI hiring identified as critical path

Divergent Perspectives (Lower Agreement)

Models disagreed on:

Topic Mistral Large 3 GPT-5.1 Claude Opus 4.5 Gemini 3 Pro Preview
Second Priority Security scanning Performance tools Security scanning Security scanning
Timeline 18 months 12 months 24 months 15 months
Hiring Needs 8-10 ML engineers 12-15 engineers 6-8 specialists 10-12 engineers
Partnership Focus Security vendors Cloud providers Enterprise clients Developer community

Insight: The 49% similarity between GPT-5.1 and Claude Opus 4.5 is remarkably low for flagship models—they approach problems quite differently. This makes combining them in an ensemble particularly powerful, as they bring genuinely diverse perspectives. Meanwhile, Mistral Large 3 aligns most closely with Claude (71%), suggesting similar depth and reasoning style.

Step 5: Output Quality Deep Dive

After 3 rounds of competitive refinement, AI Crucible's arbiter model (Gemini 2.5 Flash) evaluated each response across five dimensions: Accuracy, Creativity, Clarity, Completeness, and Usefulness.

Multi-Dimensional Evaluation

Multi-Dimensional Evaluation Scores

Model Overall Accuracy Creativity Clarity Completeness Usefulness
Mistral Large 3 🏆 9.4/10 9.5/10 9/10 9.5/10 9.5/10 9.5/10
Claude Opus 4.5 9.2/10 9.5/10 8.5/10 9/10 9.5/10 9.5/10
Gemini 3 Pro Preview 8.8/10 9/10 8/10 9.5/10 8.5/10 9/10
GPT-5.1 8.1/10 8.5/10 7.5/10 8.5/10 8/10 8/10

Surprise winner: Mistral Large 3 achieved the highest overall score (9.4/10), narrowly beating Claude Opus 4.5 (9.2/10) while costing 14x less.

Mistral Large 3 Response (9.4/10) — Winner

Arbiter Evaluation:

"Mistral Large 3 delivers an outstanding response, characterized by its strong strategic thesis and 'Critical Refinement' sections that provide deep justification for its recommendations. The 'Layered Approach' for build vs. buy is very clear, and the phased roadmap is exceptionally detailed with specific deliverables and success metrics. The 'Immediate Next Steps (First 30 Days)' is highly actionable, and the 'Narrative for Series C' is a brilliant touch for investor positioning. It combines strategic depth with actionable steps, making it incredibly useful and clear. It matches Claude in completeness and usefulness, while arguably having a slightly more compelling overall narrative and structure."

Strengths:

Weaknesses:

Claude Opus 4.5 Response (9.2/10)

Arbiter Evaluation:

"Claude Opus 4.5 is exceptionally thorough and detailed, particularly in its 'Technical Complexity & Build vs. Buy Analysis' with specific cost estimates, which is a significant value-add. Its emphasis on 'noise reduction' and 'context' as core differentiators is very insightful. The risk matrix is comprehensive, and the 'Implementation Roadmap' is incredibly granular and actionable, providing weekly/monthly steps. This model excels in completeness and usefulness, offering a highly practical guide. The only minor drawback is that the executive summary is slightly less 'punchy' than Mistral or Gemini."

Strengths:

Weaknesses:

Gemini 3 Pro Preview Response (8.8/10)

Arbiter Evaluation:

"Gemini 3 Pro Preview provides a very clear, concise, and strategically sound recommendation. Its 'Intelligent Quality Gate' narrative is compelling and easy to grasp. The differentiation between AI generation and remediation is well-articulated. The build vs. buy strategy is practical, emphasizing wrapping open-source tools. The risk mitigation table is excellent. It scores high on clarity and usefulness due to its directness and actionable advice. Completeness is good, covering all prompt points, though it lacks the granular cost estimates of Claude or the detailed roadmap of Mistral."

Strengths:

Weaknesses:

GPT-5.1 Response (8.1/10)

Arbiter Evaluation:

"GPT-5.1 offers a solid, well-structured response with good market sizing data and a clear 'Intelligent Code Guardian' brand. Its resource allocation section is quite detailed, which is helpful. The go-to-market strategy is well-defined. However, it feels slightly less innovative in its framing compared to Gemini or Mistral, and the technical build vs. buy details are not as deep as Claude. It's a very competent response but doesn't quite reach the strategic depth or actionable specificity of the top performers."

Strengths:

Weaknesses:

Full Cost Breakdown (3 Rounds)

Here's the complete cost breakdown for running 3 rounds of competitive refinement plus arbiter analysis:

Cost Breakdown

Model Costs (3 Rounds)

Model Input Tokens Output Tokens Total Cost
Mistral Large 3 14,512 10,702 $0.0233
GPT-5.1 17,855 3,985 $0.0622
Gemini 3 Pro Preview 17,899 5,502 $0.1018
Claude Opus 4.5 147 11,835 $0.3160
Subtotal (Models) 50,413 32,024 $0.5033

Arbiter Cost

Component Input Tokens Output Tokens Cost
Final Analysis & Comparison 11,277 3,411 $0.0119

Total Session Cost

Metric Value
Input Tokens 61,690
Output Tokens 35,435
Total Tokens 97,125
Total Cost $0.5271

Key insight: Mistral Large 3 produced the second-most output tokens (10,702) while costing only $0.0233—that's 14x cheaper than Claude ($0.3160) for comparable quality scores (9.4 vs 9.2). Claude's cost was driven by its extensive output (11,835 tokens) at premium pricing.

Step 6: Practical Recommendations

Based on the comparative analysis, here's what we learned:

When to Choose Each Model

Choose Mistral Large 3 (9.4/10) When: 🏆

Avoid when: Speed is critical (slowest at 118.15s in Round 1)

Choose Claude Opus 4.5 (9.2/10) When:

Avoid when: Cost is constrained ($0.3160 for 3 rounds)

Choose Gemini 3 Pro Preview (8.8/10) When:

Avoid when: Maximum quality or lowest cost is required

Choose GPT-5.1 (8.1/10) When:

Avoid when: You need comprehensive strategic analysis or highest quality

Ensemble Strategy: Combining Flagships

The real power emerges when you combine these flagship models. Based on our Round 1 similarity analysis, GPT-5.1 + Claude Opus 4.5 offers maximum diversity (49% similarity) while Claude + Mistral provides depth with redundancy (71% similarity).

Recommended Ensemble Configurations

For maximum perspective diversity:

Combine: GPT-5.1 + Claude Opus 4.5 (49% similarity)
Or: Gemini 3 Pro Preview + Claude Opus 4.5 (51% similarity)

Why: Lowest similarity = most diverse perspectives
Estimated cost: ~$0.11-0.13 per round

For comprehensive analysis at low cost:

Primary: Mistral Large 3 (comprehensive + cheap)
Reviewer: Gemini 3 Pro Preview (fast + different perspective)
Synthesizer: Gemini 2.5 Flash (fast, cheap)

Why: 61% similarity provides diversity; total cost ~$0.03
Estimated cost: ~$0.08-0.10 for 3 rounds

For speed-optimized workflows:

Primary: Gemini 3 Pro Preview (25s response)
Alternative: GPT-5.1 (71% similar, 80s response)
Synthesizer: Gemini 2.5 Flash (fast, cheap)

Why: High similarity ensures coherence; Gemini's speed dominates
Estimated cost: ~$0.04-0.06 for 3 rounds

Benchmark Summary

Metric Mistral Large 3 GPT-5.1 Claude Opus 4.5 Gemini 3 Pro Preview Winner
Overall Quality 9.4/10 🏆 8.1/10 9.2/10 8.8/10 Mistral
Speed (Round 1) 118.15s 80.80s 106.43s 25.82s Gemini
Cost (3 Rounds) $0.0233 $0.0622 $0.3160 $0.1018 Mistral
Context Window 256K 128K 200K 2M Gemini

Quality Ratings (After 3 Rounds)

Metric Mistral Large 3 GPT-5.1 Claude Opus 4.5 Gemini 3 Pro Preview Winner
Accuracy 9.5/10 8.5/10 9.5/10 9/10 Tie
Creativity 9/10 7.5/10 8.5/10 8/10 Mistral
Clarity 9.5/10 8.5/10 9/10 9.5/10 Tie
Completeness 9.5/10 8/10 9.5/10 8.5/10 Tie
Usefulness 9.5/10 8/10 9.5/10 9/10 Tie
Open Weights Yes No No No Mistral

Key Takeaways

1. Mistral Large 3 Wins on Quality AND Cost

The biggest surprise: Mistral Large 3 achieved the highest overall score (9.4/10), narrowly beating Claude Opus 4.5 (9.2/10) while costing 14x less ($0.0233 vs $0.3160 for 3 rounds). This is exceptional value—flagship quality at budget prices.

2. Each Flagship Has a Clear Strength

Priority Best Choice Why
Quality Mistral Large 3 🏆 9.4/10 overall, highest creativity
Speed Gemini 3 Pro Preview 25.82s (4x faster than next)
Cost Mistral Large 3 $0.0233 for 3 rounds (14x cheaper)
Context Gemini 3 Pro Preview 2M tokens (8x larger than GPT)
Open Weights Mistral Large 3 Only flagship with open weights

3. Mistral Matches Claude's Depth at 14x Lower Cost

With 71% response similarity and nearly identical completeness/usefulness scores (9.5/10), Mistral Large 3 and Claude Opus 4.5 deliver comparable depth. But Mistral does it for $0.0233 vs Claude's $0.3160—a 14x cost advantage with marginally better quality.

4. Gemini 3 Pro Preview Dominates Speed

Gemini completed in 25.82 seconds—over 4x faster than the next model (GPT-5.1 at 80.80s). For latency-sensitive applications, Gemini is the clear choice despite higher cost than Mistral.

5. GPT-5.1 Underperformed Expectations

GPT-5.1 scored lowest (8.1/10) with the weakest creativity (7.5/10) and completeness (8/10). Its concise responses (984 tokens in Round 1) may lack the depth needed for complex strategic analysis compared to the more comprehensive outputs from Mistral and Claude.

6. Open Weights Matter for Enterprise

Mistral Large 3 is the only flagship model with open weights. Combined with its best-in-class performance, this makes Mistral uniquely compelling for enterprises requiring data sovereignty, on-premise deployment, or regulatory compliance.

All Available Mistral Models

With this integration, AI Crucible now supports the full Mistral model family:

Model Use Case Input Cost Output Cost Context
Mistral Large 3 Flagship analysis $0.50/1M $1.50/1M 256K
Mistral Medium 3.1 Balanced performance $0.40/1M $1.20/1M 128K
Mistral Small 3.2 Fast, cost-effective $0.10/1M $0.30/1M 128K
Ministral 8B Edge deployment $0.10/1M $0.10/1M 128K
Ministral 3B Ultra-efficient edge $0.04/1M $0.04/1M 128K

Recommendation: Start with Mistral Large 3 for complex tasks, use Mistral Small 3.2 for high-volume applications, and consider Ministral models for edge or cost-critical deployments.

Try It Yourself

Ready to test Mistral Large 3 against other flagships? Here's a quick start:

  1. Go to AI Crucible Dashboard
  2. Select: Mistral Large 3, GPT-5.1, Claude Opus 4.5, Gemini 3 Pro Preview
  3. Choose Strategy: Competitive Refinement or Expert Panel
  4. Enter your prompt and click Run
  5. Analyze: Review speed, cost, similarity, and quality

Suggested test prompts:

Related Articles

Methodology Notes

Test conditions:

Metrics explained:

Why Round 1 matters: In Round 1, all models receive the identical prompt with no prior context. This provides the fairest comparison of raw model capabilities. Subsequent rounds include previous responses as context, which can skew comparisons.

Image Generation Prompts

For article hero image (1200x630px, aspect ratio 1.91:1, light/clean aesthetic for technical audience):

Prompt 1: Four Pillars Comparison

Create a clean, minimal illustration of four vertical pillars of different heights on white background, each representing a flagship AI model. From left to right: orange pillar (Mistral), green pillar (GPT/OpenAI), orange-brown pillar (Claude/Anthropic), blue pillar (Gemini/Google). Pillars have subtle geometric patterns. Use only Mistral orange (#FF7000), GPT green (#10A37F), Claude orange (#D97757), and Gemini blue (#4285F4) colors. Flat design style with no gradients, no shadows, no depth effects. Technical diagram aesthetic with clean lines. The image represents comparing four flagship AI models. Dimensions: 1200x630px (aspect ratio 1.91:1), horizontal landscape orientation. No text in image.

Prompt 2: Radar Chart Comparison

Create a clean, minimal illustration of four overlapping radar/spider charts on light gray background (#F8F9FA), each in a different color representing a flagship AI model. Charts show different performance profiles with varying vertices. Use Mistral orange (#FF7000), GPT green (#10A37F), Claude orange (#D97757), and Gemini blue (#4285F4). Flat design with no gradients, semi-transparent fills. Technical diagram aesthetic showing model comparison across multiple dimensions. Dimensions: 1200x630px (aspect ratio 1.91:1), horizontal landscape orientation. No text in image.

Prompt 3: Racing Lanes

Create a clean, minimal illustration of four horizontal racing lanes on white background, each with a simple geometric marker (diamond, circle, triangle, square) at different positions along the track. Lane colors from top to bottom: Mistral orange (#FF7000), GPT green (#10A37F), Claude orange (#D97757), Gemini blue (#4285F4). Finish line on the right side. Flat design style with no gradients, no shadows. Technical aesthetic representing model performance comparison. Dimensions: 1200x630px (aspect ratio 1.91:1), horizontal landscape orientation. No text in image.

Prompt 4: Connected Nodes

Create a clean, minimal illustration of four interconnected circular nodes on white background arranged in a slight arc. Each node is a different color: Mistral orange (#FF7000), GPT green (#10A37F), Claude orange (#D97757), Gemini blue (#4285F4). Thin gray lines connect all nodes to each other, forming a network. Flat design with no gradients, no shadows. Simple, technical aesthetic representing AI model ecosystem and comparisons. Dimensions: 1200x630px (aspect ratio 1.91:1), horizontal landscape orientation. No text in image.

Style requirements for all prompts: