How Chinese LLMs enhance food production, quality assurance, and safety compliance through TokenEase's unified API
The global food processing industry generates over $4 trillion annually and faces unprecedented challenges: ensuring safety across complex supply chains, meeting evolving regulatory requirements, reducing waste, and responding to shifting consumer preferences toward clean labels and sustainability. Chinese LLMs like DeepSeek-V4, GLM-4, and Qwen3 provide powerful reasoning, structured analysis, and multi-source data synthesis capabilities that are transforming how food companies manage quality, safety, and innovation.
Developing new food products requires balancing taste, nutrition, cost, shelf stability, and regulatory compliance across dozens of ingredients and processing parameters. LLMs can analyze ingredient databases, predict formulation outcomes, optimize for multiple constraints, and generate structured product specification documents.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a senior food scientist and product development specialist with expertise in clean-label formulation, sensory science, and nutritional optimization. Develop recipes that meet multiple constraints including cost targets, nutritional profiles, shelf stability, processing feasibility, and regulatory compliance. Output professional formulation documents with scaling calculations and manufacturing instructions."},
{"role": "user", "content": """Develop a clean-label protein snack bar formulation for a health-conscious millennial demographic:
Product positioning:
- Target consumer: Ages 25-40, fitness enthusiasts, clean-label seekers
- Occasion: Post-workout recovery, afternoon energy boost
- Price target: $2.49 retail, COGS target: $0.65
- Positioning: Premium but accessible, gender-neutral
Clean-label requirements:
- Maximum 8 ingredients (excluding vitamins/minerals)
- No artificial sweeteners, colors, flavors, or preservatives
- No high-fructose corn syrup
- No hydrogenated oils
- Recognizable ingredient names only
- Organic preferred (where cost permits)
Nutritional targets (per 60g bar):
- Protein: 15-20g
- Fiber: 8-12g
- Sugar: 8g or less (naturally occurring acceptable)
- Calories: 220-260
- Fat: 8-12g (primarily unsaturated)
- Sodium: <200mg
- Allergen: Must be nut-free (peanut and tree nut)
Functional requirements:
- Shelf stable 12 months at ambient temperature
- Texture: Chewy, not crumbly, not sticky
- No refrigeration required
- Individually wrapped, transportable
- Resistant to melting up to 90F (32C)
Available ingredient inventory (preferred suppliers):
- Proteins: Brown rice protein, pea protein isolate, pumpkin seed protein, sunflower seed protein, collagen peptides
- Binders: Dates, brown rice syrup, honey, maple syrup, tapioca syrup
- Fats: Sunflower seed butter, pumpkin seed butter, coconut oil, cocoa butter
- Fiber: Chicory root fiber, soluble tapioca fiber, oats (gluten-free), psyllium husk
- Flavors: Cocoa powder, vanilla extract, cinnamon, sea salt, freeze-dried fruit powders
- Texture: Crisped quinoa, puffed amaranth, chia seeds, flax seeds, sunflower seeds
- Coatings: Dark chocolate (70% cacao), yogurt coating, cocoa dusting
Provide:
1. Complete formulation with percentages and weights (per bar and per 1,000 bar batch)
2. Ingredient specification sheet for each component
3. Nutritional analysis (macro and micro breakdown)
4. Allergen assessment and precautionary labeling
5. Processing instructions (mixing, forming, cutting, coating, packaging)
6. Shelf-life prediction and stability testing protocol
7. Sensory evaluation criteria and target scores
8. Cost breakdown per bar with sourcing recommendations
9. Scale-up considerations (pilot to commercial production)
10. Packaging and label claim recommendations"""}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
Hazard Analysis Critical Control Points (HACCP) is the foundation of modern food safety management. LLMs can analyze production processes, identify potential hazards, determine critical control points, establish monitoring procedures, and generate complete HACCP plans that meet regulatory requirements.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "glm-4",
"messages": [
{"role": "system", "content": "You are a certified food safety professional (CP-FS) and HACCP specialist with expertise in FDA regulations, USDA standards, and Global Food Safety Initiative (GFSI) schemes. Develop comprehensive HACCP plans, conduct hazard analyses, identify critical control points, and establish monitoring and verification procedures. Output professional HACCP documentation suitable for regulatory inspection and third-party audits."},
{"role": "user", "content": """Develop a complete HACCP plan for a ready-to-eat (RTE) fresh salad production facility:
Facility overview:
- Production volume: 50,000 RTE salad bowls per day
- Products: Mixed green salads, grain bowls, protein salads
- Distribution: Refrigerated trucks to retail and foodservice
- Shelf life: 5 days at 4C (40F)
- Facility: 25,000 sq ft, USDA-inspected, SQF Level 2 certified
Process flow (simplified):
1. Receiving: Fresh produce, proteins (pre-cooked), grains, dressings, packaging
2. Cold storage (4C): Raw material holding
3. Washing/sanitizing: Triple wash for leafy greens (potable water, chlorinated wash, rinse)
4. Drying: Centrifugal dryers
5. Preparation: Chopping, portioning proteins and grains
6. Assembly: Bowl filling by weight
7. Dressing addition (separate sealed packet or pre-dressed)
8. Sealing: Modified atmosphere packaging (MAP)
9. Metal detection
10. Labeling and coding
11. Case packing
12. Refrigerated storage (4C)
13. Shipping
Known hazards (preliminary):
- Biological: Salmonella, Listeria monocytogenes, E. coli O157:H7, Norovirus
- Chemical: Pesticide residues, cleaning chemical residues, allergen cross-contact
- Physical: Metal fragments, plastic pieces, glass, stones
Allergen profile:
- Products contain: Wheat (croutons), soy (dressings), milk (cheese, dressings), eggs (dressings), fish (anchovy in Caesar dressing), shellfish (shrimp in some SKUs), tree nuts (almonds, walnuts), sesame
- Facility also processes: Peanuts (separate line, shared warehouse)
Regulatory framework:
- FDA Food Safety Modernization Act (FSMA)
- FDA Food Code 2022
- USDA guidelines for products containing meat/poultry
- State department of agriculture inspection
- SQF Code Edition 9
Provide:
1. Complete hazard analysis for each process step (biological, chemical, physical)
2. Critical Control Points (CCPs) with justification
3. Critical limits for each CCP with scientific basis
4. Monitoring procedures (what, how, frequency, who)
5. Corrective action procedures for each CCP deviation
6. Verification procedures (calibration, record review, testing)
7. Record-keeping requirements
8. Prerequisite programs (GMPs, SSOPs, supplier approval)
9. Environmental monitoring program (Listeria spp., ATP)
10. Allergen control program
11. Traceability and recall procedures
12. Training requirements and competency verification"""}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
Food recalls can cost companies millions in direct costs and reputational damage. LLMs can analyze traceability data, model contamination scenarios, generate recall scope assessments, and draft regulatory notifications within minutes of a potential issue being identified.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "qwen3-235b-a22b",
"messages": [
{"role": "system", "content": "You are a crisis management and food recall specialist with expertise in FDA recall classifications, FSMA traceback requirements, and supply chain analytics. Analyze contamination events, determine recall scope, model distribution impacts, and draft regulatory communications. Output structured recall action plans with risk assessments and stakeholder communication strategies."},
{"role": "user", "content": """A potential contamination has been identified in a pasta sauce product. Conduct traceability analysis and recall scope assessment:
Product: "Mama's Marinara" pasta sauce, 24oz jar
SKU: MM-24-OG
Lot code format: MMYY-BB-FFF (month-year, batch, facility)
Contamination details:
- Issue: Undeclared soy protein found in product testing
- Detection: Internal QA testing, not consumer complaint
- Concentration: 0.3% soy protein by weight
- Affected lots: 0626-12-03, 0626-13-03, 0626-14-03 (June 2026, batches 12-14, Facility 03)
- Root cause suspected: Shared processing line with soy-containing Alfredo sauce, inadequate changeover cleaning
Production data:
- Facility 03 production rate: 8,000 jars per batch
- Batch size: 8,000 jars (24oz)
- Affected lots: 3 batches = 24,000 jars total
- Production dates: June 12-14, 2026
- Shelf life: 18 months (best by: December 2027)
Distribution data:
- Shipped to 3 distribution centers: East Coast (40%), Midwest (35%), West Coast (25%)
- East Coast DC: Shipped to 450 retail locations (grocery chains, independent stores)
- Midwest DC: Shipped to 380 retail locations
- West Coast DC: Shipped to 290 retail locations
- Foodservice accounts: 85 restaurants, 12 institutional customers
- Direct-to-consumer: 1,200 units via company website
- Export: 2,000 units to Canada
Retail partners:
- Major grocery chains: Kroger (18% of volume), Safeway (15%), Publix (12%), HEB (8%), Wegmans (5%)
- Club stores: Costco (10%), Sam's Club (7%)
- Independent grocers: 25% of volume
- Online: Amazon (8%), company website (2%)
Consumer data:
- Estimated units sold to consumers: 14,000 (as of discovery date, June 20)
- Estimated units still in retail inventory: 8,000
- Estimated units in consumer homes: 14,000
- Units in distribution centers: 2,000
Allergen risk assessment:
- Soy is one of top 9 allergens (FDA)
- Estimated soy-allergic consumers in US: 0.4% of population
- Severe reaction (anaphylaxis) risk: Low at 0.3% concentration, but not zero
- No consumer complaints received to date
Provide:
1. Recall classification recommendation (Class I, II, or III with FDA criteria mapping)
2. Affected product scope (lots, dates, quantities, distribution)
3. Consumer risk assessment (exposure modeling, health impact)
4. Regulatory notification requirements and timelines
5. Draft FDA notification (Form FDA 3500A or equivalent)
6. Retailer notification strategy (template communications)
7. Consumer notification plan (press release, website, social media)
8. Traceability gap analysis (what data is missing, how to improve)
9. Financial impact estimate (direct recall costs + lost sales + liability)
10. Corrective action plan (facility, process, supplier)
11. Media response strategy and talking points
12. Timeline: hour-by-hour action plan for first 72 hours"""}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
Food processing equipment downtime can cost thousands per hour and jeopardize entire production batches. LLMs can analyze equipment sensor data, maintenance logs, and operational parameters to predict failures before they occur and optimize maintenance schedules.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "You are a food processing equipment reliability engineer specializing in thermal processing systems, pasteurization, and HTST/UHT equipment. Analyze operational data, maintenance histories, and sensor trends to predict equipment failures, optimize maintenance intervals, and ensure regulatory compliance with pasteurization requirements. Output professional reliability reports with actionable recommendations."},
{"role": "user", "content": """Analyze pasteurizer performance data and recommend maintenance actions:
Equipment: APV Plate Pasteurizer, 15,000 L/hr capacity, HTST system
Installation date: January 2023 (3.5 years old)
Last major overhaul: December 2025
Operating schedule: 20 hours/day, 6 days/week
Sensor data (past 30 days, daily averages):
- Flow rate (L/hr): 14,200 | 14,150 | 14,100 | 14,050 | 14,000 | 13,950 | 13,900 | 13,850 | 13,800 | 13,750 | 13,700 | 13,650 | 13,600 | 13,550 | 13,500 | 13,450 | 13,400 | 13,350 | 13,300 | 13,250 | 13,200 | 13,150 | 13,100 | 13,050 | 13,000 | 12,950 | 12,900 | 12,850 | 12,800 | 12,750
- Hot water inlet temp (C): 78.2 | 78.1 | 78.0 | 77.9 | 77.8 | 77.7 | 77.6 | 77.5 | 77.4 | 77.3 | 77.2 | 77.1 | 77.0 | 76.9 | 76.8 | 76.7 | 76.6 | 76.5 | 76.4 | 76.3 | 76.2 | 76.1 | 76.0 | 75.9 | 75.8 | 75.7 | 75.6 | 75.5 | 75.4 | 75.3
- Product outlet temp (C): 72.0 | 72.0 | 72.0 | 72.0 | 72.0 | 71.9 | 71.9 | 71.9 | 71.8 | 71.8 | 71.8 | 71.7 | 71.7 | 71.7 | 71.6 | 71.6 | 71.6 | 71.5 | 71.5 | 71.5 | 71.4 | 71.4 | 71.4 | 71.3 | 71.3 | 71.3 | 71.2 | 71.2 | 71.2 | 71.1
- Regeneration efficiency (%): 92.5 | 92.3 | 92.1 | 91.9 | 91.7 | 91.5 | 91.3 | 91.1 | 90.9 | 90.7 | 90.5 | 90.3 | 90.1 | 89.9 | 89.7 | 89.5 | 89.3 | 89.1 | 88.9 | 88.7 | 88.5 | 88.3 | 88.1 | 87.9 | 87.7 | 87.5 | 87.3 | 87.1 | 86.9 | 86.7
- Pressure drop across plates (bar): 0.85 | 0.87 | 0.89 | 0.91 | 0.93 | 0.95 | 0.97 | 0.99 | 1.01 | 1.03 | 1.05 | 1.07 | 1.09 | 1.11 | 1.13 | 1.15 | 1.17 | 1.19 | 1.21 | 1.23 | 1.25 | 1.27 | 1.29 | 1.31 | 1.33 | 1.35 | 1.37 | 1.39 | 1.41 | 1.43
- Motor current (A): 42.0 | 42.1 | 42.2 | 42.3 | 42.4 | 42.5 | 42.6 | 42.7 | 42.8 | 42.9 | 43.0 | 43.1 | 43.2 | 43.3 | 43.4 | 43.5 | 43.6 | 43.7 | 43.8 | 43.9 | 44.0 | 44.1 | 44.2 | 44.3 | 44.4 | 44.5 | 44.6 | 44.7 | 44.8 | 44.9
- Vibration (mm/s RMS): 2.1 | 2.1 | 2.2 | 2.2 | 2.3 | 2.3 | 2.4 | 2.4 | 2.5 | 2.5 | 2.6 | 2.6 | 2.7 | 2.7 | 2.8 | 2.8 | 2.9 | 2.9 | 3.0 | 3.0 | 3.1 | 3.1 | 3.2 | 3.2 | 3.3 | 3.3 | 3.4 | 3.4 | 3.5 | 3.5
Maintenance history:
- Jan 2025: Plate gasket replacement (all plates)
- Jun 2025: Pump seal replacement
- Dec 2025: Full CIP system overhaul, new temperature sensors
- Feb 2026: Flow meter calibration
- Apr 2026: Plate inspection (minor fouling, chemically cleaned)
Regulatory requirements:
- Pasteurization temperature: minimum 72C for 15 seconds (HTST)
- FDA PMO (Pasteurized Milk Ordinance) compliance required
- Daily calibration verification of temperature sensors
- Monthly flow rate verification
Provide:
1. Equipment health score (0-100) with trend analysis
2. Failure mode prediction (next 30/60/90 days)
3. Root cause analysis of observed degradation patterns
4. Recommended immediate maintenance actions
5. Optimal maintenance interval recommendations
6. Regulatory compliance risk assessment
7. Production impact analysis if unplanned shutdown occurs
8. Spare parts inventory recommendations
9. Energy efficiency impact of current degradation
10. Capital replacement timeline recommendation"""}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
Managing allergens across complex formulations and multi-product facilities is one of the most critical food safety challenges. LLMs can analyze ingredient declarations, assess cross-contact risks, verify label accuracy against formulations, and generate precautionary allergen labeling recommendations.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "glm-4",
"messages": [
{"role": "system", "content": "You are a food allergen management specialist with expertise in FDA FALCPA, EU Regulation 1169/2011, and global allergen labeling requirements. Assess allergen risks in manufacturing facilities, evaluate cross-contact potential, verify label compliance, and recommend risk mitigation strategies. Output professional allergen management reports with VITAL-style risk assessments."},
{"role": "user", "content": """Conduct an allergen risk assessment for a multi-product bakery facility:
Facility overview:
- 45,000 sq ft production facility
- 3 production lines operating 16 hours/day
- 12 active SKUs, 6 seasonal SKUs
- SQF certified, FDA-registered, USDA-inspected (some products)
- 180 employees, 3 shifts
Product portfolio:
Line 1 (Bread & Rolls):
- SKU-A: White sandwich bread (wheat, soy lecithin, sesame)
- SKU-B: Whole wheat bread (wheat, soy lecithin, honey)
- SKU-C: Dinner rolls (wheat, milk, egg, sesame)
- SKU-D: Hamburger buns (wheat, soy, sesame, milk)
Line 2 (Pastries & Cakes):
- SKU-E: Vanilla cupcakes (wheat, egg, milk, soy)
- SKU-F: Chocolate muffins (wheat, egg, milk, soy, may contain tree nuts)
- SKU-G: Cinnamon rolls (wheat, milk, egg, soy)
- SKU-H: Blueberry scones (wheat, milk, soy, may contain tree nuts)
Line 3 (Cookies & Crackers):
- SKU-I: Sugar cookies (wheat, egg, milk, soy)
- SKU-J: Peanut butter cookies (wheat, peanut, egg, milk, soy)
- SKU-K: Cheese crackers (wheat, milk, soy)
- SKU-L: Graham crackers (wheat, honey, soy, may contain milk)
Allergens present in facility:
- Wheat (all products)
- Milk (SKU-C, D, E, F, G, H, I, J, K)
- Egg (SKU-C, E, F, G, H, I, J)
- Soy (all products via lecithin or oil)
- Peanut (SKU-J only)
- Tree nuts (almond flour in SKUs F, H as may contain)
- Sesame (SKU-A, C, D)
- Honey (SKU-B, L)
Production schedule (weekly):
- Line 1: SKU-A (Mon-Tue), SKU-B (Wed), SKU-C (Thu), SKU-D (Fri)
- Line 2: SKU-E (Mon), SKU-F (Tue-Wed), SKU-G (Thu), SKU-H (Fri)
- Line 3: SKU-I (Mon-Tue), SKU-J (Wed), SKU-K (Thu), SKU-L (Fri)
- Changeover cleaning: 2 hours between SKUs, CIP protocol
- Deep clean: Saturday (8 hours, full breakdown)
Recent issues:
- March 2026: Consumer complaint - allergic reaction to SKU-I (sugar cookies), consumer has peanut allergy. Investigation found peanut protein residue on Line 3 from inadequate changeover after SKU-J.
- May 2026: Label audit found SKU-F declared "may contain tree nuts" but label omitted "may contain peanuts" despite shared line with SKU-J.
Provide:
1. Allergen matrix (product x allergen presence and risk level)
2. Cross-contact risk assessment by production line and changeover
3. VITAL-style quantitative risk assessment for peanut cross-contact
4. Label compliance review for all 12 SKUs (accuracy, completeness, regulatory)
5. Sanitation verification recommendations (ATP, protein swabs, allergen-specific)
6. Production scheduling optimization to minimize cross-contact risk
7. Employee training program outline (allergen awareness, GMPs)
8. Supplier verification requirements for allergen-free claims
9. Corrective action for March and May incidents
10. Preventive action program (allergen control plan)
11. Consumer communication strategy for label updates
12. Regulatory notification assessment (is FDA notification required?)"""}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
Understanding how consumers perceive food products is essential for successful launches. LLMs can analyze sensory panel data, correlate descriptive attributes with consumer liking scores, identify key drivers of preference, and generate product optimization recommendations.
import requests
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_KEY"},
json={
"model": "qwen3-235b-a22b",
"messages": [
{"role": "system", "content": "You are a sensory scientist and consumer insights analyst specializing in food product optimization. Analyze descriptive sensory panel data, consumer hedonic scores, and preference mapping to identify key drivers of liking and recommend formulation adjustments. Output professional sensory reports with statistical interpretation and actionable product development guidance."},
{"role": "user", "content": """Analyze sensory and consumer data for a new plant-based yogurt launch:
Product: Coconut-oat base yogurt, vanilla flavor, 150g cup
Target: Dairy yogurt alternative for health-conscious consumers
Competitor benchmark: Silk, Oatly, Chobani Non-Dairy
Trained sensory panel results (n=12 panelists, 15-point intensity scales):
Prototype A (current formula):
- Vanilla flavor intensity: 8.2
- Coconut flavor intensity: 6.5
- Sourness: 4.1
- Sweetness: 9.3
- Creaminess/mouthfeel: 7.8
- Viscosity: 6.2
- Chalkiness: 3.1
- Beany/off-note: 2.8
- Overall dairy-likeness: 5.4
- Aftertaste pleasantness: 6.9
Prototype B (higher fat):
- Vanilla flavor intensity: 7.9
- Coconut flavor intensity: 7.2
- Sourness: 3.8
- Sweetness: 8.7
- Creaminess/mouthfeel: 9.1
- Viscosity: 7.5
- Chalkiness: 1.9
- Beany/off-note: 2.1
- Overall dairy-likeness: 7.8
- Aftertaste pleasantness: 8.4
Prototype C (added protein):
- Vanilla flavor intensity: 7.5
- Coconut flavor intensity: 5.8
- Sourness: 4.5
- Sweetness: 8.1
- Creaminess/mouthfeel: 6.9
- Viscosity: 7.8
- Chalkiness: 4.2
- Beany/off-note: 3.5
- Overall dairy-likeness: 6.1
- Aftertaste pleasantness: 5.8
Competitor benchmarks (sensory panel):
- Silk Vanilla: Creaminess 8.5, Sweetness 9.0, Dairy-likeness 7.2, Aftertaste 7.5
- Oatly Vanilla: Creaminess 9.2, Sweetness 7.8, Dairy-likeness 8.5, Aftertaste 8.8
- Chobani ND Vanilla: Creaminess 8.8, Sweetness 8.5, Dairy-likeness 8.9, Aftertaste 8.6
Consumer acceptance test (n=180, 9-point hedonic scale):
Prototype A:
- Overall liking: 6.2 (like slightly)
- Purchase intent (5-point): 3.1 (might buy)
- Key likes: "good vanilla flavor" (45%), "smooth texture" (38%)
- Key dislikes: "too sweet" (52%), "not creamy enough" (41%), "slight chalky" (28%)
Prototype B:
- Overall liking: 7.4 (like moderately)
- Purchase intent: 3.8 (probably would buy)
- Key likes: "very creamy" (62%), "good mouthfeel" (55%), "pleasant aftertaste" (48%)
- Key dislikes: "coconut too strong" (22%), "a bit rich" (18%)
Prototype C:
- Overall liking: 5.8 (neither like nor dislike)
- Purchase intent: 2.7 (might or might not buy)
- Key likes: "good protein content" (35%), "thick texture" (31%)
- Key dislikes: "chalky" (58%), "beany taste" (44%), "not sweet enough" (38%)
Demographics of consumer panel:
- Age: 18-34 (35%), 35-54 (45%), 55+ (20%)
- Current dairy yogurt consumers: 72%
- Current plant-based yogurt consumers: 28%
- Primary purchase driver: Taste (48%), Health (32%), Sustainability (20%)
Cost constraints:
- Prototype A: $0.42 per 150g cup (COGS)
- Prototype B: $0.51 per 150g cup (COGS)
- Prototype C: $0.48 per 150g cup (COGS)
- Target retail: $1.99 (COGS target: max $0.50)
Provide:
1. Sensory profile comparison (radar chart description)
2. Consumer liking driver analysis (PLS or PREFMAP interpretation)
3. Optimal formulation recommendation (blend or single prototype)
4. Key formulation adjustments to maximize liking
5. Target demographic alignment by prototype
6. Cost-optimized formulation recommendation
7. Benchmark gap analysis vs top competitors
8. Claims substantiation (what can be claimed based on data)
9. Recommended package design cues based on sensory profile
10. Scale-up considerations from pilot to production
11. Quality control specifications for production release
12. Recommended consumer validation test before launch"""}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
| Model | Best For | Context | Multi-modal |
|---|---|---|---|
| DeepSeek-V4 | Recipe formulation, product development, equipment diagnostics | 128K | Text + Image |
| GLM-4 | HACCP plans, compliance documentation, allergen management | 128K | Text + Image |
| GLM-4V | Label verification, packaging review, visual defect detection | 8K | Vision |
| Qwen3-235B | Traceability analysis, recall management, sensory statistics | 128K | Text |
Access DeepSeek-V4, GLM-4, Qwen3, and more through a single API. Get started in minutes with unified authentication, consistent response formats, and 40% lower pricing than alternatives.