o3 vs Magistral Medium
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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o3 by OpenAI and Magistral Medium 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, Magistral Medium holds a notable pricing advantage for token ingestion. On output generation, Magistral Medium is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, o3 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, o3 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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o3
Deliberate ReasonerThe o-series flagship reasoner — strong on difficult maths, coding and multi-step analysis.
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
Magistral Medium
Transparent ReasonerMistral's specialized reasoning engine engineered with inspectable chain-of-thought, mathematical proof synthesis, and deliberate decision trees.
$1.00 / 1M
$3.50 / 1M
128K tokens
45.2%
Key Differences at a Glance
- Magistral Medium is 2.2x cheaper for API usage (input + output tokens combined).
- o3 has a larger context window (200K vs 128K tokens).
- o3 is faster at 75 tok/s vs 50 tok/s.
- o3 leads on SWE-bench coding (69.1% vs 45.2%).
| Feature | o3 | Magistral Medium |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 51 |
Provider | OpenAI | Mistral AI |
Input Price Cost per million input tokens | $2.00 / 1M | $1.00 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $3.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.72 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 100K | 33K |
Throughput Speed Tokens generated per second | 75 tok/s | 50 tok/s |
SWE-bench Verified Real-world software engineering | 69.1% | 45.2% |
GPQA Diamond Graduate-level reasoning | 76.5% | 67.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.25 / 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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
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.
o3 · Strengths & Key Advantages
- Powerful deliberate reasoning with vision support
- Significantly cheaper than o1-pro for comparable accuracy
- Adjustable reasoning effort to trade cost against quality
- Strong at tool-augmented agentic coding loops
Considerations & Trade-offs
- Outperformed by GPT-5 on reasoning and coding
- Thinking tokens inflate the effective cost per task
Magistral Medium · Strengths & Key Advantages
- Transparent, auditable internal reasoning trace before final synthesis
- Substantial improvements on competitive mathematics and algorithmic proofs
- Full 128K context window with deliberate tool planning
Considerations & Trade-offs
- Higher token output overhead due to extended reasoning traces
- Text-only input without native image or video processing
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o3 | Magistral Medium |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.35 | $0.65 |
Output Tokens / Task Tokens generated per task | 48K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 22K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | o3 | Magistral Medium |
|---|---|---|
| Official API Model ID (SDK) | o3 | magistral-medium-latest |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Mistral AI REST API (OpenAI-Compatible) |
| Release Date | 2025-04 | 2025-03 |
| Knowledge Cutoff | June 2024 | January 2025 |
| Architecture | Reasoning transformer with adjustable thinking effort | Reinforcement-Learned Reasoning Transformer |
| Parameters | Confidential | Confidential (~70B) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 200K window | Flat rate ($1/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Cached input at $0.25/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Mistral AI Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
o3 vs Magistral Medium: Quick Answers
Which is more intelligent, o3 or Magistral Medium?
o3 is more intelligent. It scores 56 compared with 51 on the composite Intelligence Index.
Which model is faster?
o3 is faster, generating 75 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Magistral Medium is cheaper at $0.72 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Magistral Medium has lower latency, with a time to first token of 0.85s compared with 2.80s.
Which model has a larger context window?
o3 has a larger context window, supporting 200K tokens compared with 128K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose o3 if your priority is Hard maths, science and agentic coding where accuracy beats latency. Its performance in demanding tasks justifies its operational token cost. Choose Magistral Medium if your workflow prioritizes Complex audit reports, legal clause contradiction analysis, and mathematical logic proofs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does o3 compare to Magistral Medium in API token pricing and cost per token?+
o3 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens, whereas Magistral Medium costs $1.00 input and $3.50 output. Overall, Magistral Medium 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: o3 or Magistral Medium?+
o3 supports a context window of 200K tokens (~150k words), while Magistral Medium supports 128K tokens (~96k words). Therefore, o3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: o3 or Magistral Medium?+
o3 scores 69.1% on SWE-bench Verified while Magistral Medium scores 45.2%. For autonomous software engineering and multi-file debugging, o3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o3 and Magistral Medium?+
o3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Magistral Medium provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o3 and Magistral Medium?+
o3 generates approximately 75 tokens per second, while Magistral Medium delivers 50 tokens per second. In latency-sensitive production environments, o3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o3 and Magistral Medium to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Magistral Medium to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to o3. 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