Mistral Medium 3.5 vs GPT-5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Mistral Medium 3.5 by Mistral AI and GPT-5.5 by OpenAI 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, Mistral Medium 3.5 holds a notable pricing advantage for token ingestion. On output generation, Mistral Medium 3.5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5.5 provides an expanded memory horizon with 1,050K tokens. In latency-sensitive production environments, GPT-5.5 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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Mistral Medium 3.5
Enterprise BalancedFrontier enterprise generalist offering 262K context, multimodal vision, hybrid reasoning, and open weights under attractive $0.40/$2.00 pricing.
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
GPT-5.5
Default Frontier GPTThe default frontier GPT for coding, computer use, research and knowledge work.
$5.00 / 1M
$30.00 / 1M
1.05M tokens
81%
Key Differences at a Glance
- Mistral Medium 3.5 is 13.8x cheaper for API usage (input + output tokens combined).
- GPT-5.5 has a larger context window (1M vs 262K tokens).
- GPT-5.5 is faster at 112 tok/s vs 90 tok/s.
- GPT-5.5 leads on SWE-bench coding (81.0% vs 54.0%).
| Feature | Mistral Medium 3.5 | GPT-5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 84 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.40 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $30.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.35 / 1M | $4.35 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1.05M tokens~1,575 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 90 tok/s | 112 tok/s |
SWE-bench Verified Real-world software engineering | 54% | 81% |
GPQA Diamond Graduate-level reasoning | 68% | 80% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.50 / 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.
Mistral Medium 3.5 · Strengths & Key Advantages
- Expansive 262K context capacity with near-zero needle retrieval degradation
- Highly attractive $0.40 in / $2.00 out pricing for an enterprise flagship
- Open-weights availability with native visual and reasoning depth
Considerations & Trade-offs
- Trails proprietary multi-trillion parameter giants on bleeding-edge coding
- Requires dedicated enterprise hardware for full local deployment
GPT-5.5 · Strengths & Key Advantages
- Best-in-class long-horizon agentic reliability
- 1M-token context with solid recall
- Strong coding, research and computer-use performance
- Best-in-class reliability for long autonomous runs
Considerations & Trade-offs
- Roughly twice the price of GPT-5.4
- Output pricing makes high-volume use expensive
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Medium 3.5 | GPT-5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $3.90 |
Output Tokens / Task Tokens generated per task | 38K | 64K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 37K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Medium 3.5 | GPT-5.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-medium-3-5-latest | gpt-5-5 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2026-04 |
| Knowledge Cutoff | February 2026 | December 2025 |
| Architecture | Enterprise Hybrid Reasoning MoE | Default frontier unified reasoning model |
| Parameters | Confidential (~80B MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.4/1M in) across full 262K window | Flat rate ($5/1M in) across full 1050K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Automatic prefix caching at $0.5/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Mistral Medium 3.5 vs GPT-5.5: Quick Answers
Which is more intelligent, Mistral Medium 3.5 or GPT-5.5?
GPT-5.5 is more intelligent. It scores 84 compared with 55 on the composite Intelligence Index.
Which model is faster?
GPT-5.5 is faster, generating 112 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $4.35 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mistral Medium 3.5 has lower latency, with a time to first token of 0.45s compared with 1.18s.
Which model has a larger context window?
GPT-5.5 has a larger context window, supporting 1.05M tokens compared with 262.1K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Mistral Medium 3.5 if your priority is Enterprise intelligence hubs, sovereign multilingual workflows, and multi-turn analytical agents. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.5 if your workflow prioritizes Frontier agents and demanding professional knowledge work. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Mistral Medium 3.5 compare to GPT-5.5 in API token pricing and cost per token?+
Mistral Medium 3.5 costs $0.40 per 1M input tokens and $2.00 per 1M output tokens, whereas GPT-5.5 costs $5.00 input and $30.00 output. Overall, Mistral Medium 3.5 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: Mistral Medium 3.5 or GPT-5.5?+
Mistral Medium 3.5 supports a context window of 262K tokens (~197k words), while GPT-5.5 supports 1,050K tokens (~788k words). Therefore, GPT-5.5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Medium 3.5 or GPT-5.5?+
Mistral Medium 3.5 scores 54% on SWE-bench Verified while GPT-5.5 scores 81%. For autonomous software engineering and multi-file debugging, GPT-5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Medium 3.5 and GPT-5.5?+
Mistral Medium 3.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Mistral Medium 3.5 and GPT-5.5?+
Mistral Medium 3.5 generates approximately 90 tokens per second, while GPT-5.5 delivers 112 tokens per second. In latency-sensitive production environments, GPT-5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Medium 3.5 and GPT-5.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Medium 3.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.5. 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