fix: implement robust model mapping using slugs and normalized names
This commit is contained in:
laurent
104
README.md
104
README.md
@@ -1,76 +1,76 @@
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# Table des Modèles Mammouth.ai
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*Mise à jour automatique via Artificial Analysis & Mammouth Public API*
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*Généré automatiquement à partir des benchmarks d'Artificial Analysis et des tarifs Mammouth.ai.*
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Dernière mise à jour : 2026-02-22 16:45:41
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Dernière mise à jour : 2026-02-22 16:47:56
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## Coding
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| Modèle | Prix (In / Out / 1M) | Intelligence Index | Vitesse (TPS) |
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| Modèle | Prix (In / Out / 1M) | Score (Intelligence) | Vitesse (TPS) |
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| :--- | :--- | :--- | :--- |
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| grok-code-fast-1 | $0.20 / $1.50 | N/A | N/A |
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| grok-code-fast-1 | $0.20 / $1.50 | **23.7** | 314.1 |
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| qwen3-coder | $0.22 / $0.95 | N/A | N/A |
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| codestral-2508 | $0.30 / $0.90 | N/A | N/A |
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| qwen3-coder-flash | $0.50 / $2.00 | N/A | N/A |
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| qwen3-coder-plus | $1.80 / $9.00 | N/A | N/A |
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| qwen3-coder-flash | $0.50 / $2.00 | N/A | N/A |
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## Agents
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| Modèle | Prix (In / Out / 1M) | Intelligence Index | Vitesse (TPS) |
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| Modèle | Prix (In / Out / 1M) | Score (Intelligence) | Vitesse (TPS) |
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| :--- | :--- | :--- | :--- |
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| sonar-pro | $3.00 / $15.00 | **15.2** | 129.2 |
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| sonar-deep-research | $2.00 / $8.00 | N/A | N/A |
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| sonar-pro | $3.00 / $15.00 | N/A | N/A |
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## General
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| Modèle | Prix (In / Out / 1M) | Intelligence Index | Vitesse (TPS) |
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| Modèle | Prix (In / Out / 1M) | Score (Intelligence) | Vitesse (TPS) |
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| :--- | :--- | :--- | :--- |
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| text-embedding-3-small | $0.02 / $0.00 | N/A | N/A |
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| gpt-5-nano | $0.05 / $0.40 | N/A | N/A |
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| llama-4-scout | $0.08 / $0.50 | N/A | N/A |
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| gpt-4.1-nano | $0.10 / $0.40 | N/A | N/A |
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| mistral-small-3.2-24b-instruct | $0.10 / $0.30 | N/A | N/A |
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| gemini-3-pro-preview | $2.00 / $12.00 | **48.4** | 138.8 |
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| kimi-k2.5 | $0.60 / $3.00 | **46.7** | 44.8 |
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| claude-opus-4-6 | $5.00 / $25.00 | **46.4** | 66.8 |
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| claude-opus-4-5 | $5.00 / $25.00 | **43.0** | 65.1 |
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| grok-4-0709 | $3.00 / $15.00 | **41.4** | 39.4 |
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| gpt-5-mini | $0.25 / $2.00 | **41.0** | 75.3 |
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| kimi-k2-thinking | $0.55 / $2.50 | **40.7** | 86.9 |
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| gemini-3-flash-preview | $0.50 / $3.00 | **35.1** | 177.9 |
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| gemini-2.5-pro | $2.50 / $15.00 | **34.5** | 158.9 |
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| o4-mini | $1.10 / $4.40 | **33.0** | 133.8 |
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| claude-4-sonnet-20250522 | $3.00 / $15.00 | **33.0** | 72.8 |
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| deepseek-v3.2 | $0.27 / $0.42 | **32.1** | 49.1 |
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| claude-3-7-sonnet-20250219 | $3.00 / $15.00 | **30.8** | N/A |
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| deepseek-v3.1-terminus | $0.27 / $1.00 | **28.4** | N/A |
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| deepseek-v3.1 | $0.27 / $1.00 | **28.0** | N/A |
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| deepseek-r1-0528 | $0.50 / $2.18 | **27.0** | N/A |
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| gpt-5-nano | $0.05 / $0.40 | **26.7** | 130.9 |
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| kimi-k2-instruct | $0.50 / $2.50 | **26.2** | 40.8 |
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| gpt-4.1 | $2.00 / $8.00 | **25.6** | 103.9 |
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| grok-3 | $3.00 / $15.00 | **25.0** | 67.7 |
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| grok-4-1-fast | $0.20 / $0.50 | **23.5** | 119.7 |
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| mistral-large-3 | $0.50 / $1.50 | **22.7** | 55.9 |
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| gpt-4.1-mini | $0.40 / $1.60 | **22.4** | 77.4 |
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| mistral-medium-3.1 | $0.40 / $2.00 | **21.1** | 86.8 |
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| gemini-2.5-flash | $0.30 / $2.50 | **20.5** | 235.9 |
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| mistral-medium-3 | $0.40 / $2.00 | **18.7** | 90.2 |
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| claude-3-5-haiku-20241022 | $0.80 / $4.00 | **18.7** | 46.4 |
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| llama-4-maverick | $0.15 / $0.60 | **18.3** | 126.7 |
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| gpt-4o | $2.50 / $10.00 | **17.3** | 168.6 |
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| deepseek-v3-0324 | $0.25 / $1.00 | **16.4** | N/A |
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| claude-3-5-sonnet-20241022 | $3.00 / $15.00 | **15.9** | N/A |
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| llama-4-scout | $0.08 / $0.50 | **13.5** | 158.6 |
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| gpt-4.1-nano | $0.10 / $0.40 | **12.9** | 141.9 |
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| mistral-large-2411 | $2.00 / $6.00 | **9.9** | N/A |
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| text-embedding-3-large | $0.13 / $0.00 | N/A | N/A |
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| llama-4-maverick | $0.15 / $0.60 | N/A | N/A |
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| grok-4-1-fast | $0.20 / $0.50 | N/A | N/A |
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| gpt-5-mini | $0.25 / $2.00 | N/A | N/A |
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| deepseek-v3-0324 | $0.25 / $1.00 | N/A | N/A |
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| deepseek-v3.2 | $0.27 / $0.42 | N/A | N/A |
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| deepseek-v3.1 | $0.27 / $1.00 | N/A | N/A |
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| deepseek-v3.1-terminus | $0.27 / $1.00 | N/A | N/A |
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| deepseek-v3.2-exp | $0.27 / $0.41 | N/A | N/A |
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| gemini-2.5-flash-image | $0.30 / $2.50 | N/A | N/A |
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| grok-3-mini | $0.30 / $0.50 | N/A | N/A |
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| gemini-2.5-flash | $0.30 / $2.50 | N/A | N/A |
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| grok-4-fast-non-reasoning | $0.40 / $1.00 | N/A | N/A |
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| mistral-medium-3 | $0.40 / $2.00 | N/A | N/A |
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| gpt-4.1-mini | $0.40 / $1.60 | N/A | N/A |
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| mistral-medium-3.1 | $0.40 / $2.00 | N/A | N/A |
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| gemini-3-flash-preview | $0.50 / $3.00 | N/A | N/A |
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| kimi-k2-instruct | $0.50 / $2.50 | N/A | N/A |
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| mistral-large-3 | $0.50 / $1.50 | N/A | N/A |
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| deepseek-r1-0528 | $0.50 / $2.18 | N/A | N/A |
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| kimi-k2-thinking | $0.55 / $2.50 | N/A | N/A |
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| kimi-k2.5 | $0.60 / $3.00 | N/A | N/A |
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| claude-3-5-haiku-20241022 | $0.80 / $4.00 | N/A | N/A |
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| claude-haiku-4-5 | $1.00 / $5.00 | N/A | N/A |
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| o4-mini | $1.10 / $4.40 | N/A | N/A |
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| gpt-5-chat | $1.25 / $10.00 | N/A | N/A |
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| gpt-5.1-chat | $1.25 / $10.00 | N/A | N/A |
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| gpt-5.2-chat | $1.75 / $14.00 | N/A | N/A |
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| mistral-large-2411 | $2.00 / $6.00 | N/A | N/A |
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| gpt-4.1 | $2.00 / $8.00 | N/A | N/A |
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| gemini-3-pro-image-preview | $2.00 / $12.00 | N/A | N/A |
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| gemini-3-pro-preview | $2.00 / $12.00 | N/A | N/A |
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| gpt-4o | $2.50 / $10.00 | N/A | N/A |
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| gemini-2.5-pro | $2.50 / $15.00 | N/A | N/A |
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| claude-3-7-sonnet-20250219 | $3.00 / $15.00 | N/A | N/A |
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| claude-4-sonnet-20250522 | $3.00 / $15.00 | N/A | N/A |
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| grok-4-fast-non-reasoning | $0.40 / $1.00 | N/A | N/A |
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| claude-sonnet-4-5 | $3.00 / $15.00 | N/A | N/A |
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| claude-3-5-sonnet-20241022 | $3.00 / $15.00 | N/A | N/A |
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| grok-3 | $3.00 / $15.00 | N/A | N/A |
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| grok-4-0709 | $3.00 / $15.00 | N/A | N/A |
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| claude-opus-4-6 | $5.00 / $25.00 | N/A | N/A |
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| claude-opus-4-5 | $5.00 / $25.00 | N/A | N/A |
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| gpt-5.1-chat | $1.25 / $10.00 | N/A | N/A |
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| claude-haiku-4-5 | $1.00 / $5.00 | N/A | N/A |
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| gemini-2.5-flash-image | $0.30 / $2.50 | N/A | N/A |
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| claude-opus-4-1-20250805 | $15.00 / $75.00 | N/A | N/A |
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| deepseek-v3.2-exp | $0.27 / $0.41 | N/A | N/A |
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| gpt-5.2-chat | $1.75 / $14.00 | N/A | N/A |
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| grok-3-mini | $0.30 / $0.50 | N/A | N/A |
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| mistral-small-3.2-24b-instruct | $0.10 / $0.30 | N/A | N/A |
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| gemini-3-pro-image-preview | $2.00 / $12.00 | N/A | N/A |
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| text-embedding-3-small | $0.02 / $0.00 | N/A | N/A |
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128
update_models.py
128
update_models.py
@@ -2,17 +2,19 @@ import os
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import requests
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import json
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import time
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import re
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from dotenv import load_dotenv
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# Charger .env.global
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load_dotenv("../.env.global")
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AIANALASYS_APIKEY = os.getenv("AIANALASYS_APIKEY")
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def get_mammouth_models():
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# URL correcte fournie par l'utilisateur
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url = "https://api.mammouth.ai/public/models"
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try:
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response = requests.get(url, verify=False) # verify=False au cas où il y a des soucis de certifs
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# Désactiver les warnings InsecureRequest car verify=False est utilisé
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requests.packages.urllib3.disable_warnings()
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response = requests.get(url, verify=False)
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response.raise_for_status()
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return response.json().get('data', [])
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except Exception as e:
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@@ -27,99 +29,97 @@ def get_aa_data():
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response.raise_for_status()
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return response.json().get('data', [])
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except Exception as e:
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print(f"Error fetching Artificial Analysis data: {e}")
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print(f"Error fetching AA data: {e}")
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return []
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def clean_id(model_id):
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# Nettoyage agressif pour favoriser le mapping
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id_clean = re.sub(r'-\d{4,8}', '', model_id.lower())
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id_clean = id_clean.replace('-latest', '').replace('-preview', '').replace('-instruct', '')
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return id_clean.strip()
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def generate_markdown(models_data):
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categories = {}
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for m in models_data:
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cat = m.get('category', 'General')
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if cat not in categories:
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categories[cat] = []
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if cat not in categories: categories[cat] = []
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categories[cat].append(m)
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md = "# Table des Modèles Mammouth.ai\n\n"
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md += "*Mise à jour automatique via Artificial Analysis & Mammouth Public API*\n\n"
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md += "*Généré automatiquement à partir des benchmarks d'Artificial Analysis et des tarifs Mammouth.ai.*\n\n"
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md += f"Dernière mise à jour : {time.strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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order = ['Coding', 'Agents', 'General']
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sorted_cats = sorted(categories.keys(), key=lambda x: order.index(x) if x in order else 99)
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for cat in sorted_cats:
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models = categories[cat]
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# Tri : Score (desc), puis Prix (asc)
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models.sort(key=lambda x: (x.get('score') or 0, -(x.get('price_in') or 999)), reverse=True)
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md += f"## {cat}\n\n"
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md += "| Modèle | Prix (In / Out / 1M) | Intelligence Index | Vitesse (TPS) |\n"
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md += "| Modèle | Prix (In / Out / 1M) | Score (Intelligence) | Vitesse (TPS) |\n"
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md += "| :--- | :--- | :--- | :--- |\n"
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models = categories[cat]
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# Tri : Score (desc), puis Nom
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models.sort(key=lambda x: (x.get('score') or 0), reverse=True)
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for m in models:
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p_in = f"${m['price_in']:.2f}"
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p_out = f"${m['price_out']:.2f}"
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score = f"**{m['score']:.1f}**" if m['score'] else "N/A"
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speed = f"{m['speed']:.1f}" if m['speed'] else "N/A"
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md += f"| {m['name']} | {p_in} / {p_out} | {score} | {speed} |\n"
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score_str = f"**{m['score']:.1f}**" if m['score'] else "N/A"
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speed_str = f"{m['speed']:.1f}" if m['speed'] else "N/A"
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md += f"| {m['name']} | ${m['price_in']:.2f} / ${m['price_out']:.2f} | {score_str} | {speed_str} |\n"
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md += "\n"
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return md
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def main():
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print("Fetching Mammouth public models...")
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mammouth_models = get_mammouth_models()
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print(f"Found {len(mammouth_models)} models from Mammouth.")
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print("Fetching data from Mammouth and Artificial Analysis...")
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m_models = get_mammouth_models()
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aa_data = get_aa_data()
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print("Fetching Artificial Analysis data...")
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aa_raw = get_aa_data()
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# Mapping AA
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# Mapping table (slug -> data)
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aa_map = {}
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for aa_m in aa_raw:
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m_id = aa_m.get('model_id', '').lower()
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m_name = aa_m.get('model_name', '').lower()
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if m_id: aa_map[m_id] = aa_m
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if m_name: aa_map[m_name] = aa_m
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for aa_m in aa_data:
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slug = aa_m.get('slug', '').lower()
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name = aa_m.get('name', '').lower()
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if slug: aa_map[slug] = aa_m
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if name: aa_map[name] = aa_m
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enriched_models = []
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for m in mammouth_models:
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enriched = []
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for m in m_models:
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m_id = m.get('id', '')
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info = m.get('model_info', {})
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if not m_id: continue
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# Mapping intelligent
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m_id_low = m_id.lower()
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aa_info = aa_map.get(m_id_low)
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m_id_clean = clean_id(m_id)
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short_id = m_id_clean.split('/')[-1]
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# Si pas de match exact, on cherche une correspondance partielle
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# Match mapping
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aa_info = aa_map.get(m_id_clean) or aa_map.get(short_id)
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# Recherche floue (ex: claude-3-5-sonnet -> claude-3.5-sonnet)
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if not aa_info:
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for key in aa_map:
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if key in m_id_low or m_id_low in key:
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# On vérifie que ce n'est pas un faux positif (ex: gpt-4 vs gpt-4-turbo)
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if abs(len(key) - len(m_id_low)) < 5:
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aa_info = aa_map[key]
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break
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normalized_m_id = m_id_clean.replace('-', '').replace('.', '')
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for key, val in aa_map.items():
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if key.replace('-', '').replace('.', '') == normalized_m_id:
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aa_info = val
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break
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price_in = float(info.get('input_cost_per_token', 0)) * 1000000
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price_out = float(info.get('output_cost_per_token', 0)) * 1000000
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category = "General"
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if any(x in m_id_clean for x in ['coding', 'code', 'starcoder', 'codestral', 'coder']):
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category = "Coding"
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elif any(x in m_id_clean for x in ['agent', 'hermes', 'tool', 'function', 'sonar']):
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category = "Agents"
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# Extraction des prix
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try:
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price_in = float(info.get('input_cost_per_token', 0)) * 1000000
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price_out = float(info.get('output_cost_per_token', 0)) * 1000000
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except:
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price_in, price_out = 0, 0
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score = None
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speed = None
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if aa_info:
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evals = aa_info.get('evaluations', {})
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score = evals.get('artificial_analysis_intelligence_index')
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speed = aa_info.get('median_output_tokens_per_second')
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# On prend le score coding si c'est la catégorie, sinon intelligence index
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score = evals.get('artificial_analysis_coding_index') if category == "Coding" else None
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if not score:
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score = evals.get('artificial_analysis_intelligence_index')
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# Catégorisation
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category = "General"
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if any(x in m_id_low for x in ['coding', 'code', 'starcoder', 'coder', 'codestral']):
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category = "Coding"
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elif any(x in m_id_low for x in ['agent', 'hermes', 'tool', 'function', 'sonar']):
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category = "Agents"
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speed = aa_info.get('median_output_tokens_per_second')
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enriched_models.append({
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enriched.append({
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'name': m_id,
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'price_in': price_in,
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'price_out': price_out,
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@@ -128,13 +128,13 @@ def main():
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'category': category
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})
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|
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# Filtrer les modèles (on garde tout ce qui a un prix ou un score)
|
||||
final_list = [m for m in enriched_models if m['price_in'] > 0 or m['score'] is not None]
|
||||
# On ne garde que les modèles avec prix > 0
|
||||
final = [x for x in enriched if x['price_in'] > 0]
|
||||
|
||||
with open("README.md", "w", encoding="utf-8") as f:
|
||||
f.write(generate_markdown(final_list))
|
||||
f.write(generate_markdown(final))
|
||||
|
||||
print(f"Done! README.md updated with {len(final_list)} models.")
|
||||
print(f"Success! {len(final)} models processed.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user