Pixtral 12B vs Mistral Large 2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Pixtral 12B by Mistral AI and Mistral Large 2 by Mistral AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Pixtral 12B holds a notable pricing advantage for token ingestion. On output generation, Pixtral 12B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Pixtral 12B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Pixtral 12B
Open VLMMistral's premier open 12-billion parameter multimodal model natively trained on text and images with arbitrary aspect ratio vision encoding.
$0.15 / 1M
$0.15 / 1M
128K tokens
28%
Mistral Large 2
European Frontier FlagshipMistral’s 123-billion parameter flagship engineered for world-class coding (84% HumanEval), advanced reasoning, and enterprise agentic tool use with 128K context.
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
Key Differences at a Glance
- Pixtral 12B is 22.8x cheaper for API usage (input + output tokens combined).
- Pixtral 12B is faster at 85 tok/s vs 75 tok/s.
- Mistral Large 2 leads on SWE-bench coding (38.0% vs 28.0%).
| Feature | Pixtral 12B | Mistral Large 2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 40 | 46 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $0.15 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $6.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $1.70 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 28% | 38% |
GPQA Diamond Graduate-level reasoning | 43.8% | 54% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $1.00 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Pixtral 12B · Strengths & Key Advantages
- Native image comprehension supporting multiple images and arbitrary aspect ratios
- Open weights under Apache 2.0 license for unrestricted local hosting
- Strong visual document OCR, chart parsing, and diagram understanding
Considerations & Trade-offs
- Smaller capacity limits complex multi-step reasoning on dense schematics
- Video and audio modalities not supported
Mistral Large 2 · Strengths & Key Advantages
- Directly rivals GPT-4o and Claude 3.5 Sonnet on reasoning and code generation
- Dense 123B parameter model optimized for single-node serving efficiency
- First-class JSON schema outputs and multi-step agentic function calling
Considerations & Trade-offs
- Text-only without native image vision capabilities
- Requires enterprise multi-GPU server for local hosting
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Pixtral 12B | Mistral Large 2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.20 |
Output Tokens / Task Tokens generated per task | 25K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Pixtral 12B | Mistral Large 2 |
|---|---|---|
| Official API Model ID (SDK) | pixtral-12b-latest | mistral-large-2-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-07 |
| Knowledge Cutoff | August 2024 | July 2024 |
| Architecture | Multimodal Vision-Language Transformer | 123B Dense Frontier Transformer |
| Parameters | 12B | 123B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.15/1M in) across full 128K window | Flat rate ($2/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $1/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Pixtral 12B vs Mistral Large 2: Quick Answers
Which is more intelligent, Pixtral 12B or Mistral Large 2?
Mistral Large 2 is more intelligent. It scores 46 compared with 40 on the composite Intelligence Index.
Which model is faster?
Pixtral 12B is faster, generating 85 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Pixtral 12B is cheaper at $0.08 per 1M tokens blended, compared with $1.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Pixtral 12B has lower latency, with a time to first token of 0.45s compared with 0.52s.
Verdict
Verdict & Model Selection Recommendation: Choose Pixtral 12B if your priority is Document OCR, visual inspection agents, and open multimodal pipelines. Its performance in demanding tasks justifies its operational token cost. Choose Mistral Large 2 if your workflow prioritizes Enterprise sovereignty in the EU, complex multilingual reasoning, and autonomous code pipelines. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Pixtral 12B compare to Mistral Large 2 in API token pricing and cost per token?+
Pixtral 12B costs $0.15 per 1M input tokens and $0.15 per 1M output tokens, whereas Mistral Large 2 costs $2.00 input and $6.00 output. Overall, Pixtral 12B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Pixtral 12B or Mistral Large 2?+
Both Pixtral 12B and Mistral Large 2 offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Pixtral 12B or Mistral Large 2?+
Pixtral 12B scores 28% on SWE-bench Verified while Mistral Large 2 scores 38%. For autonomous software engineering and multi-file debugging, Mistral Large 2 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Pixtral 12B and Mistral Large 2?+
Pixtral 12B is optimized for low-latency direct generation. In contrast, Mistral Large 2 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Pixtral 12B and Mistral Large 2?+
Pixtral 12B generates approximately 85 tokens per second, while Mistral Large 2 delivers 75 tokens per second. In latency-sensitive production environments, Pixtral 12B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Pixtral 12B and Mistral Large 2 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Pixtral 12B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Large 2. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs