Codestral vs DeepSeek-Coder-V2 236B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Codestral by Mistral AI and DeepSeek-Coder-V2 236B by DeepSeek 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, DeepSeek-Coder-V2 236B holds a notable pricing advantage for token ingestion. On output generation, DeepSeek-Coder-V2 236B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Codestral provides an expanded memory horizon with 256K tokens. In latency-sensitive production environments, Codestral 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.
किसी भी मॉडल को बदलें और तुलना करें
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Codestral
ओपन-वेट्स मॉडलMistral AI द्वारा विकसित Codestral एक उन्नत AI मॉडल है, जिसमें 256K टोकन का कॉन्टेक्स्ट विंडो और $0.2/1M इनपुट तथा $0.6/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.20 / 1M
$0.60 / 1M
256K tokens
28%
DeepSeek-Coder-V2 236B
ओपन-वेट्स मॉडलDeepSeek द्वारा विकसित DeepSeek-Coder-V2 236B एक उन्नत AI मॉडल है, जिसमें 128K टोकन का कॉन्टेक्स्ट विंडो और $0.14/1M इनपुट तथा $0.28/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।
$0.14 / 1M
$0.28 / 1M
128K tokens
42.1%
एक नज़र में मुख्य अंतर
- DeepSeek-Coder-V2 236B is 1.9x cheaper for API usage (input + output tokens combined).
- Codestral has a larger context window (256K vs 128K tokens).
- Codestral is faster at 110 tok/s vs 74 tok/s.
- DeepSeek-Coder-V2 236B leads on SWE-bench coding (42.1% vs 28.0%).
| Feature | Codestral | DeepSeek-Coder-V2 236B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 40 | 42 |
Provider | Mistral AI | DeepSeek |
Input Price Cost per million input tokens | $0.20 / 1M | $0.14 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.28 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.14 / 1M | $0.07 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 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 | 110 tok/s | 74 tok/s |
SWE-bench Verified Real-world software engineering | 28% | 42.1% |
GPQA Diamond Graduate-level reasoning | 42% | 49.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.01 / 1M |
किस क्षेत्र में कौन आगे है?
At-a-glance decision frameworkसत्यापित बेंचमार्क स्कोर तुलना
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.
लेटेंसी और रिस्पॉन्स समय
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
रियल-टाइम टोकन लागत सिम्युलेटर
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Codestral · Strengths & Key Advantages
- Mistral AI की उन्नत Code-Optimized Transformer with 32K Window आर्किटेक्चर और 256K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0.2/1M इनपुट, $0.6/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
DeepSeek-Coder-V2 236B · Strengths & Key Advantages
- DeepSeek की उन्नत 236B Mixture-of-Experts (21B active) आर्किटेक्चर और 128K कॉन्टेक्स्ट विंडो क्षमता
- किफायती और पारदर्शी API मूल्य ($0.14/1M इनपुट, $0.28/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
- जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
- बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
- विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Codestral | DeepSeek-Coder-V2 236B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.14 |
Output Tokens / Task Tokens generated per task | 24K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
तकनीकी विशिष्टताएँ (Specs)
Architecture, release timeline, capabilities, and modalities.
| Feature | Codestral | DeepSeek-Coder-V2 236B |
|---|---|---|
| Official API Model ID (SDK) | codestral-latest | deepseek-chat |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-05 | 2024-06 |
| Knowledge Cutoff | May 2024 | December 2023 |
| Architecture | Code-Optimized Transformer with 32K Window | 236B Mixture-of-Experts (21B active) |
| Parameters | 22B | 236B |
| 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 ($0.2/1M in) across full 256K window | Flat rate ($0.14/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Automatic prefix caching at $0.014/1M (no extra cache-write fee) |
| 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 14 GB VRAM (4-bit Quantized) · 47 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 146 GB VRAM (4-bit Quantized) · 507 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 | text |
| Output Modalities | text | text |
Codestral vs DeepSeek-Coder-V2 236B: त्वरित प्रश्नोत्तर (FAQ)
Which is more intelligent, Codestral or DeepSeek-Coder-V2 236B?
DeepSeek-Coder-V2 236B is more intelligent. It scores 42 compared with 40 on the composite Intelligence Index.
Which model is faster?
Codestral is faster, generating 110 tokens per second compared with 74 tokens per second.
Which model is cheaper to run?
DeepSeek-Coder-V2 236B is cheaper at $0.07 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Codestral has lower latency, with a time to first token of 0.35s compared with 0.51s.
Which model has a larger context window?
Codestral has a larger context window, supporting 256K tokens compared with 128K tokens.
निष्कर्ष
Verdict & Model Selection Recommendation: Choose Codestral if your priority is VS Code and JetBrains extension backends, automated test generation, and multi-language translation. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-Coder-V2 236B if your workflow prioritizes Large codebase refactoring, code translation between legacy and modern stacks, and developer API tools. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
अक्सर पूछे जाने वाले प्रश्न (FAQ)
How does Codestral compare to DeepSeek-Coder-V2 236B in API token pricing and cost per token?+
Codestral costs $0.20 per 1M input tokens and $0.60 per 1M output tokens, whereas DeepSeek-Coder-V2 236B costs $0.14 input and $0.28 output. Overall, DeepSeek-Coder-V2 236B 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: Codestral or DeepSeek-Coder-V2 236B?+
Codestral supports a context window of 256K tokens (~192k words), while DeepSeek-Coder-V2 236B supports 128K tokens (~96k words). Therefore, Codestral can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Codestral or DeepSeek-Coder-V2 236B?+
Codestral scores 28% on SWE-bench Verified while DeepSeek-Coder-V2 236B scores 42.1%. For autonomous software engineering and multi-file debugging, DeepSeek-Coder-V2 236B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Codestral and DeepSeek-Coder-V2 236B?+
Codestral is optimized for low-latency direct generation. In contrast, DeepSeek-Coder-V2 236B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Codestral and DeepSeek-Coder-V2 236B?+
Codestral generates approximately 110 tokens per second, while DeepSeek-Coder-V2 236B delivers 74 tokens per second. In latency-sensitive production environments, Codestral provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Codestral and DeepSeek-Coder-V2 236B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek-Coder-V2 236B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Codestral. 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.
स्रोत: GetInfoUs Independent AI Benchmarks & Official API Specs