GPT-4o vs Mistral Medium 3.1
Mistral Medium 3.1 is the better pick for most production and high-throughput use cases: it wins 6 of 12 benchmarks in our suite and leads in long-context, strategic analysis, constrained rewriting, safety calibration, agentic planning, and multilingual. GPT-4o ties on several core abilities but brings multimodal file support and costs roughly 5× more (input $2.5 / output $10 vs $0.4 / $2), so GPT-4o is worth considering only when its specific OpenAI ecosystem features or file-level modality matter.
openai
GPT-4o
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$10.00/MTok
modelpicker.net
mistral
Mistral Medium 3.1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
modelpicker.net
Benchmark Analysis
Our 12-test head-to-head shows Mistral Medium 3.1 wins six categories: strategic analysis (5 vs 2), constrained rewriting (5 vs 3), long context (5 vs 4), safety calibration (2 vs 1), agentic planning (5 vs 4), and multilingual (5 vs 4). These wins matter in practice: Medium 3.1’s 5/5 on long context ties it for 1st of 55 models (tied with 36 others), while GPT-4o’s 4/5 ranks 38 of 55 — meaning Medium 3.1 is more reliable for retrieval and reasoning across 30K+ token documents. On strategic analysis, Medium 3.1 ranks tied for 1st of 54; GPT-4o ranks 44 of 54, so expect Medium 3.1 to handle nuanced tradeoffs and numeric reasoning better in our tests. The two models tie on structured output (4/4), creative problem solving (3/3), tool calling (4/4), faithfulness (4/4), classification (4/4), and persona consistency (5/5) — so both produce equivalent results on schema adherence, tooling workflows, and persona stability in our suite. Note GPT-4o also has external benchmark entries: it scores 31% on SWE-bench Verified (Epoch AI), 53.3% on MATH Level 5 (Epoch AI) and 6.4% on AIME 2025 (Epoch AI); those external numbers (attributed to Epoch AI) are supplementary and show GPT-4o’s performance on select third-party math and code benchmarks in our payload. Overall, Medium 3.1 dominates where long context, strategy, and constrained rewriting matter; GPT-4o holds parity on many practical tasks but posts the external SWE-bench and math numbers listed above.
Pricing Analysis
Per the payload, GPT-4o charges $2.50 per mTok input and $10.00 per mTok output; Mistral Medium 3.1 charges $0.40 per mTok input and $2.00 per mTok output (price ratio = 5). Using a 50/50 input-output split: at 1M tokens/month (1,000 mTok) GPT-4o ≈ $6,250 vs Medium 3.1 ≈ $1,200; at 10M tokens GPT-4o ≈ $62,500 vs Medium 3.1 ≈ $12,000; at 100M tokens GPT-4o ≈ $625,000 vs Medium 3.1 ≈ $120,000. If your workload is output-heavy, gaps widen because GPT-4o’s $10/mTok output dominates costs. High-volume deployments, SaaS businesses, or any product with predictable heavy token usage should prioritize Medium 3.1 for cost efficiency; small-scale prototypes or projects that require specific OpenAI integrations may still justify GPT-4o’s higher price.
Real-World Cost Comparison
Bottom Line
Choose Mistral Medium 3.1 if you need: - Cost-effective, high-volume inference (input $0.40 / output $2 per mTok). - Best-in-suite long-context handling and strategic analysis (5/5, tied for 1st in our rankings). - Strong constrained rewriting and agentic planning. Choose GPT-4o if you need: - OpenAI ecosystem features, broader modality support (text+image+file→text) or specific OpenAI integrations and are willing to pay ~5× more (input $2.50 / output $10 per mTok). - Rough parity on structured output, tool-calling, classification, faithfulness and persona consistency where cost is a secondary concern.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.