Codestral 25.01
Mistral Commercial API / OpenDeveloped by Mistral AI · Released 2025-01-01
What are the token costs and operational benchmarks for Codestral 25.01?
Architectural Overview
Frontier foundation model developed by Mistral AI featuring Dense 256k Code Specialist architecture.
Optimal Production Use Cases
256K long-context repository indexing, fill-in-the-middle code completion, and IDE plugin backend.
Interactive Monthly Token Economics & ROI Forecaster
Model your expected production workload across prompt (input) and completion (output) tokens.
Estimated Monthly Spend
$0.3/1M in · $0.9/1M out
$0.18/1M in · $0.72/1M out
Head-to-Head Comparisons Involving Codestral 25.01
Codestral 25.01 vs Claude 3.5 Haiku
Codestral 25.01 is 62.5% cheaper for input tokens ($0.30 vs. $0.80 per 1M tokens) and $0.90 vs. $4.00 for output tokens (2.7x cost difference). In terms of operational performance, Claude 3.5 Haiku delivers faster response latency with 140ms TTFT (10ms faster than Codestral 25.01). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 40.6% for Claude 3.5 Haiku.
Codestral 25.01 vs Claude 3.5 Sonnet
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (170ms faster than Claude 3.5 Sonnet). Claude 3.5 Sonnet leads coding benchmarks at 63.7% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Claude 3.7 Sonnet
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (500ms faster than Claude 3.7 Sonnet). Claude 3.7 Sonnet leads coding benchmarks at 70.3% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Claude Opus 5
Codestral 25.01 is 94.0% cheaper for input tokens ($0.30 vs. $5.00 per 1M tokens) and $0.90 vs. $25.00 for output tokens (16.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (190ms faster than Claude Opus 5). Claude Opus 5 leads coding benchmarks at 82.4% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Composer 2.5
Codestral 25.01 is 83.3% cheaper for input tokens ($0.30 vs. $1.80 per 1M tokens) and $0.90 vs. $7.20 for output tokens (6.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (30ms faster than Composer 2.5). Composer 2.5 leads coding benchmarks at 74.6% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs DeepSeek-R1
Codestral 25.01 is 45.5% cheaper for input tokens ($0.30 vs. $0.55 per 1M tokens) and $0.90 vs. $2.19 for output tokens (1.8x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (1650ms faster than DeepSeek-R1). DeepSeek-R1 leads coding benchmarks at 49.2% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs DeepSeek-V3
DeepSeek-V3 is 53.3% cheaper for input tokens ($0.14 vs. $0.30 per 1M tokens) and $0.28 vs. $0.90 for output tokens (2.1x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (190ms faster than DeepSeek-V3). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 42.0% for DeepSeek-V3.
Codestral 25.01 vs DeepSeek-V4 Flash
DeepSeek-V4 Flash is 53.3% cheaper for input tokens ($0.14 vs. $0.30 per 1M tokens) and $0.56 vs. $0.90 for output tokens (2.1x cost difference). In terms of operational performance, DeepSeek-V4 Flash delivers faster response latency with 150ms TTFT (0ms faster than Codestral 25.01). DeepSeek-V4 Flash leads coding benchmarks at 62.4% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Fable 5
Codestral 25.01 is 85.0% cheaper for input tokens ($0.30 vs. $2.00 per 1M tokens) and $0.90 vs. $8.00 for output tokens (6.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (90ms faster than Fable 5). Fable 5 leads coding benchmarks at 58.0% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Gemini 2.0 Flash
Gemini 2.0 Flash is 66.7% cheaper for input tokens ($0.10 vs. $0.30 per 1M tokens) and $0.40 vs. $0.90 for output tokens (3.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (230ms faster than Gemini 2.0 Flash). Gemini 2.0 Flash leads coding benchmarks at 48.0% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Gemini 3.7 Flash
Gemini 3.7 Flash is 73.3% cheaper for input tokens ($0.08 vs. $0.30 per 1M tokens) and $0.32 vs. $0.90 for output tokens (3.8x cost difference). In terms of operational performance, Gemini 3.7 Flash delivers faster response latency with 75ms TTFT (75ms faster than Codestral 25.01). Gemini 3.7 Flash leads coding benchmarks at 68.2% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs GLM 5.3 Flash
GLM 5.3 Flash is 50.0% cheaper for input tokens ($0.15 vs. $0.30 per 1M tokens) and $0.50 vs. $0.90 for output tokens (2.0x cost difference). In terms of operational performance, GLM 5.3 Flash delivers faster response latency with 130ms TTFT (20ms faster than Codestral 25.01). GLM 5.3 Flash leads coding benchmarks at 54.2% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs OpenAI GPT-4o
Codestral 25.01 is 88.0% cheaper for input tokens ($0.30 vs. $2.50 per 1M tokens) and $0.90 vs. $10.00 for output tokens (8.3x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (130ms faster than OpenAI GPT-4o). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for OpenAI GPT-4o.
Codestral 25.01 vs GPT-5.6 Luna
GPT-5.6 Luna is 40.0% cheaper for input tokens ($0.18 vs. $0.30 per 1M tokens) and $0.72 vs. $0.90 for output tokens (1.7x cost difference). In terms of operational performance, GPT-5.6 Luna delivers faster response latency with 90ms TTFT (60ms faster than Codestral 25.01). GPT-5.6 Luna leads coding benchmarks at 48.5% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs GPT-5.6 Sol
Codestral 25.01 is 96.2% cheaper for input tokens ($0.30 vs. $8.00 per 1M tokens) and $0.90 vs. $32.00 for output tokens (26.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than GPT-5.6 Sol). GPT-5.6 Sol leads coding benchmarks at 79.5% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs GPT-5.6 Terra
Codestral 25.01 is 80.0% cheaper for input tokens ($0.30 vs. $1.50 per 1M tokens) and $0.90 vs. $6.00 for output tokens (5.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (60ms faster than GPT-5.6 Terra). GPT-5.6 Terra leads coding benchmarks at 65.4% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Grok 3
Codestral 25.01 is 90.0% cheaper for input tokens ($0.30 vs. $3.00 per 1M tokens) and $0.90 vs. $15.00 for output tokens (10.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (700ms faster than Grok 3). Grok 3 leads coding benchmarks at 58.5% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs xAI Grok 4.6
Codestral 25.01 is 85.0% cheaper for input tokens ($0.30 vs. $2.00 per 1M tokens) and $0.90 vs. $6.00 for output tokens (6.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (130ms faster than xAI Grok 4.6). xAI Grok 4.6 leads coding benchmarks at 76.8% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is 40.0% cheaper for input tokens ($0.18 vs. $0.30 per 1M tokens) and $0.40 vs. $0.90 for output tokens (1.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than Llama 3.3 70B Instruct). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 38.8% for Llama 3.3 70B Instruct.
Codestral 25.01 vs Mistral Large 2
Codestral 25.01 is 85.0% cheaper for input tokens ($0.30 vs. $2.00 per 1M tokens) and $0.90 vs. $6.00 for output tokens (6.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (400ms faster than Mistral Large 2). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 39.0% for Mistral Large 2.
Codestral 25.01 vs OpenAI o1
Codestral 25.01 is 98.0% cheaper for input tokens ($0.30 vs. $15.00 per 1M tokens) and $0.90 vs. $60.00 for output tokens (50.0x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (700ms faster than OpenAI o1). OpenAI o1 leads coding benchmarks at 48.9% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs o3-mini
Codestral 25.01 is 72.7% cheaper for input tokens ($0.30 vs. $1.10 per 1M tokens) and $0.90 vs. $4.40 for output tokens (3.7x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (1050ms faster than o3-mini). o3-mini leads coding benchmarks at 49.3% SWE-bench vs. 44.2% for Codestral 25.01.
Codestral 25.01 vs Microsoft Phi-4 (14B)
Microsoft Phi-4 (14B) is 60.0% cheaper for input tokens ($0.12 vs. $0.30 per 1M tokens) and $0.36 vs. $0.90 for output tokens (2.5x cost difference). In terms of operational performance, Microsoft Phi-4 (14B) delivers faster response latency with 110ms TTFT (40ms faster than Codestral 25.01). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 42.1% for Microsoft Phi-4 (14B).
Codestral 25.01 vs Qwen 2.5 72B Instruct
Codestral 25.01 is 14.3% cheaper for input tokens ($0.30 vs. $0.35 per 1M tokens) and $0.90 vs. $0.40 for output tokens (1.2x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (270ms faster than Qwen 2.5 72B Instruct). Codestral 25.01 leads coding benchmarks at 44.2% SWE-bench vs. 44.0% for Qwen 2.5 72B Instruct.
Codestral 25.01 vs Qwen 2.5 Max
Qwen 2.5 Max is 6.7% cheaper for input tokens ($0.28 vs. $0.30 per 1M tokens) and $0.84 vs. $0.90 for output tokens (1.1x cost difference). In terms of operational performance, Codestral 25.01 delivers faster response latency with 150ms TTFT (330ms faster than Qwen 2.5 Max). Both models demonstrate comparable coding benchmark scores.
Codestral 25.01 vs Qwen 3.8 Flash Next
Qwen 3.8 Flash Next is 60.0% cheaper for input tokens ($0.12 vs. $0.30 per 1M tokens) and $0.48 vs. $0.90 for output tokens (2.5x cost difference). In terms of operational performance, Qwen 3.8 Flash Next delivers faster response latency with 120ms TTFT (30ms faster than Codestral 25.01). Qwen 3.8 Flash Next leads coding benchmarks at 56.8% SWE-bench vs. 44.2% for Codestral 25.01.
Frequently Asked Questions & Query Fan-Out
How much does Codestral 25.01 cost per 1M tokens?
Codestral 25.01 costs $0.30 per million prompt (input) tokens and $0.90 per million completion (output) tokens.
What is the context window for Codestral 25.01?
Codestral 25.01 supports a maximum context window of 262,144 tokens, with a maximum single-generation output of 8,192 tokens.
What are the primary use cases for Codestral 25.01?
256K long-context repository indexing, fill-in-the-middle code completion, and IDE plugin backend.