AI sentiment analysis is one of the highest-ROI AI workflows because the inputs are small, the outputs are structured, and the business value is immediate. A product team can turn thousands of reviews into ranked complaints. A CX team can summarize support tickets by root cause. A marketing team can monitor social posts by sentiment, topic, urgency, and brand risk.
The cost is also lower than most teams expect. A basic sentiment classifier can process 1 million short text items for $23-$39 on low-cost models like GPT-5 nano or DeepSeek V4 Flash. More advanced workflows that extract topics, score urgency, cluster quotes, and produce executive summaries still usually land under $100-$400 per 1 million short items when routed correctly.
This guide breaks down the real 2026 API costs for sentiment classification, topic extraction, quote clustering, and executive summaries across product reviews, surveys, support feedback, and social listening. You’ll get per-10k and per-1M item math, model recommendations, and practical monthly budgets for common teams.
💡 Key Takeaway: Sentiment analysis is cheap when you separate the workflow into layers: low-cost models for classification, mid-tier models for extraction, and premium models only for final summaries or high-risk escalations.
The cost formula for AI sentiment analysis
AI API pricing is based on input tokens and output tokens. Input tokens are the text you send to the model: review text, survey response, support ticket, system prompt, and classification instructions. Output tokens are the model’s response: sentiment label, topic list, rationale, quote, summary, or JSON object.
The pricing formula is:
Cost = input tokens ÷ 1,000,000 × input price + output tokens ÷ 1,000,000 × output price
For sentiment analysis, output tokens matter more than many teams expect. A single label like positive is cheap. A JSON object with sentiment, confidence, topic, subtopic, urgency, explanation, and quote can be 5-10x larger than a label-only response.
Baseline token profiles used in this guide
To make the math concrete, this guide uses four practical profiles:
| Workflow | Input tokens per item | Output tokens per item | What it includes |
|---|---|---|---|
| Sentiment only | 220 | 30 | Text, compact prompt, sentiment label, confidence, polarity |
| Sentiment + topic extraction | 350 | 70 | Sentiment, topic, subtopic, issue type, confidence |
| Full feedback analysis | 550 | 100 | Sentiment, topic, urgency, product area, quote candidate, JSON |
| Long survey/support analysis | 900 | 180 | Longer text, richer extraction, rationale, multiple labels |
These profiles assume efficient batching and compact JSON output. They also include amortized prompt overhead. If every item is sent in a separate request with a long system prompt, your input tokens rise sharply. Batch similar items whenever your API and latency requirements allow it.
⚠️ Warning: The most common cost mistake is asking for long explanations on every item. Store structured labels for every record, then generate natural-language explanations only for sampled items, escalations, or summaries.
Current 2026 model pricing for sentiment workloads
For sentiment analysis, you usually do not need the most expensive reasoning model. Classification and extraction are structured language tasks. Low-cost and mid-tier models are strong enough for most review, survey, and support use cases.
Here are relevant 2026 API prices from AI Cost Check’s model data:
| Model | Provider | Input price / 1M tokens | Output price / 1M tokens | Context window | Best role |
|---|---|---|---|---|---|
| GPT-5 nano | OpenAI | $0.05 | $0.40 | 128k | Cheapest OpenAI classifier |
| Gemini 2.0 Flash-Lite | $0.075 | $0.30 | 1M | Low-cost bulk classification | |
| Mistral Small 3.2 | Mistral AI | $0.10 | $0.30 | 128k | Cheap extraction and tagging |
| DeepSeek V4 Flash | DeepSeek | $0.14 | $0.28 | 1M | Cheap high-volume analysis |
| GPT-5 mini | OpenAI | $0.25 | $2.00 | 500k | Reliable mid-tier extraction |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | Fast multi-step feedback analysis | |
| Claude Haiku 4.5 | Anthropic | $1.00 | $5.00 | 200k | Higher-quality classification and summarization |
| GPT-5 | OpenAI | $1.25 | $10.00 | 1M | Executive summaries and high-value analysis |
| Claude Sonnet 4.6 | Anthropic | $3.00 | $15.00 | 1M | Premium synthesis and executive narratives |
The best default stack for 2026 is:
- Bulk classification: GPT-5 nano, Gemini 2.0 Flash-Lite, Mistral Small 3.2, or DeepSeek V4 Flash
- Richer extraction: GPT-5 mini, Gemini 2.5 Flash, or Mistral Small 3.2
- Executive summaries: GPT-5 or Claude Sonnet 4.6
- Premium manual-review cases: Claude Sonnet 4.6 or GPT-5.2 for high-stakes escalations
Sentiment-only classification costs
Sentiment-only classification is the cheapest version of the workflow. Each item receives a label such as positive, neutral, negative, mixed, or urgent-negative. A good production schema also includes confidence and polarity score.
For this section, the assumed token profile is:
- 220 input tokens per item
- 30 output tokens per item
- 2.2M input tokens per 10k items
- 0.3M output tokens per 10k items
- 220M input tokens per 1M items
- 30M output tokens per 1M items
Cost per 10k and 1M short text items
| Model | Cost per 10k items | Cost per 1M items | Recommendation |
|---|---|---|---|
| GPT-5 nano | $0.23 | $23.00 | Best ultra-low-cost OpenAI option |
| Gemini 2.0 Flash-Lite | $0.26 | $25.50 | Best Google budget option |
| Mistral Small 3.2 | $0.31 | $31.00 | Strong low-cost classification |
| DeepSeek V4 Flash | $0.39 | $39.20 | Excellent price with 1M context |
| GPT-5 mini | $1.15 | $115.00 | Better reliability for noisy data |
| Claude Haiku 4.5 | $3.70 | $370.00 | Use when quality beats raw cost |
| GPT-5 | $5.75 | $575.00 | Use for high-value labeling, not bulk |
| Claude Sonnet 4.6 | $11.10 | $1,110.00 | Use for summaries, not every label |
The price difference is extreme. Claude Sonnet 4.6 costs 48x more than GPT-5 nano for the same sentiment-only classification profile. That does not mean Sonnet is a bad model. It means Sonnet should be reserved for synthesis, executive summaries, nuanced escalation review, and ambiguous samples.
[stat] $23 per 1M items GPT-5 nano can classify 1 million short reviews with sentiment, confidence, and polarity for about twenty-three dollars.
Recommended sentiment-only setup
Use GPT-5 nano when your team wants the cheapest OpenAI-compatible workflow. Use Gemini 2.0 Flash-Lite when your infrastructure is already on Google or you want a 1M-token context window for large batches. Use Mistral Small 3.2 when you want a low-cost classifier with balanced input and output pricing. Use DeepSeek V4 Flash when you want cheap processing plus a larger context window.
For production, run a quality audit on 500-1,000 labeled examples before processing millions of items. Use the cheapest model that clears your target accuracy threshold, then route ambiguous records to GPT-5 mini or Claude Haiku 4.5.
Sentiment plus topic extraction costs
Sentiment alone tells you whether customers are happy. Topic extraction tells you why. For reviews and surveys, topic extraction is usually worth the extra tokens because it turns unstructured text into product decisions.
A practical topic schema includes:
- Sentiment: positive, neutral, negative, mixed
- Topic: pricing, performance, delivery, UX, onboarding, support, reliability
- Subtopic: checkout bug, battery life, refund delay, mobile app crash
- Product area or team owner
- Confidence score
- Optional short quote
For this workflow, use:
- 350 input tokens per item
- 70 output tokens per item
- 3.5M input tokens per 10k items
- 0.7M output tokens per 10k items
| Model | Cost per 10k items | Cost per 1M items | Best use |
|---|---|---|---|
| GPT-5 nano | $0.46 | $45.50 | Cheap topic tagging for clean data |
| Gemini 2.0 Flash-Lite | $0.47 | $47.25 | Cheap large-batch tagging |
| Mistral Small 3.2 | $0.56 | $56.00 | Best low-cost balanced option |
| DeepSeek V4 Flash | $0.69 | $68.60 | Strong budget option |
| GPT-5 mini | $2.28 | $227.50 | Recommended default for product teams |
| Gemini 2.5 Flash | $2.80 | $280.00 | Good for faster richer extraction |
| Claude Haiku 4.5 | $7.00 | $700.00 | Higher quality for nuanced categories |
| GPT-5 | $11.38 | $1,137.50 | Reserve for high-value analysis |
The right default for sentiment plus topic extraction is GPT-5 mini when accuracy and consistency matter. At $227.50 per 1M items, it is still inexpensive relative to analyst time and gives better structured outputs than ultra-budget models on messy text.
For clean app reviews, ecommerce reviews, and short NPS comments, start with Mistral Small 3.2 or DeepSeek V4 Flash. For enterprise surveys, healthcare feedback, financial services complaints, or regulated categories, use GPT-5 mini as the baseline and escalate ambiguous items to GPT-5 or Claude Sonnet 4.6.
💡 Key Takeaway: Topic extraction is the sweet spot for business value. It usually costs 2x-5x more than sentiment-only classification, but it turns sentiment dashboards into action lists by team, product area, and root cause.
Full feedback analysis: sentiment, topics, urgency, and quote candidates
Full feedback analysis is the workflow most teams actually want after the first dashboard prototype. The model does not just label sentiment. It extracts a structured record that can drive prioritization.
A strong full-analysis JSON schema includes:
{
"sentiment": "negative",
"confidence": 0.91,
"topic": "support",
"subtopic": "refund delay",
"urgency": "high",
"customer_intent": "cancel_or_refund",
"product_area": "billing",
"quote_candidate": "I have waited three weeks for my refund.",
"recommended_team": "customer_support"
}
For this workflow, use:
- 550 input tokens per item
- 100 output tokens per item
- 5.5M input tokens per 10k items
- 1.0M output tokens per 10k items
| Model | Cost per 10k items | Cost per 1M items | Recommendation |
|---|---|---|---|
| GPT-5 nano | $0.68 | $67.50 | Cheapest full analysis for clean inputs |
| Mistral Small 3.2 | $0.85 | $85.00 | Best low-cost full pipeline |
| DeepSeek V4 Flash | $1.05 | $105.00 | Strong budget choice with 1M context |
| GPT-5 mini | $3.38 | $337.50 | Recommended default for most teams |
| Gemini 2.5 Flash | $4.15 | $415.00 | Good higher-throughput option |
| Claude Haiku 4.5 | $10.50 | $1,050.00 | Use for high-quality extraction |
| GPT-5 | $16.88 | $1,687.50 | Use for review, not bulk labeling |
| Claude Sonnet 4.6 | $31.50 | $3,150.00 | Use for summaries and escalations |
The full-analysis workflow is still affordable at scale. Processing 1 million short items with Mistral Small 3.2 costs about $85. Using GPT-5 mini costs about $337.50. That is a reasonable monthly bill for product teams, CX operations, marketplace trust teams, and brand monitoring teams.
The key is avoiding premium models for every row. Claude Sonnet 4.6 is excellent for writing a monthly executive brief, but using it for every item raises the same 1M-item workflow to $3,150.
Quote clustering and theme consolidation
Quote clustering is different from classification. Instead of scoring every item independently, you group similar feedback into themes and select representative quotes.
A cost-efficient quote clustering pipeline has three stages:
- Classify every item cheaply with sentiment, topic, urgency, and quote candidate.
- Filter to high-signal records such as negative reviews, high-confidence themes, or enterprise accounts.
- Cluster and summarize only the filtered subset using a stronger model.
This avoids sending all raw feedback into a premium model.
Recommended clustering method
For every 10,000 items, run full analysis first. Then select:
- Top 10-20% negative or urgent items
- Top 5-10% quote candidates by clarity
- Items from strategic accounts or high-revenue customer segments
- Duplicate complaints by product area
Then batch those filtered items into clustering prompts. A typical 10k-item batch may produce 1,000-2,000 candidate records, which can be summarized into themes using 40k-100k input tokens and 3k-8k output tokens.
For executive-ready clustering, a practical per-10k estimate is:
- 60,000 input tokens
- 4,000 output tokens
| Summary model | Cost per 10k-item batch | Cost per 1M items | Best use |
|---|---|---|---|
| GPT-5 mini | $0.023 | $2.30 | Cheap theme summaries |
| GPT-5 | $0.115 | $11.50 | Recommended executive summaries |
| Claude Sonnet 4.6 | $0.240 | $24.00 | Best narrative synthesis |
| Claude Opus 4.7 | $0.400 | $40.00 | Premium board-level writing |
The summary stage is tiny compared with per-item analysis. Even Claude Sonnet 4.6 costs only $24 per 1M source items under this batching profile because it summarizes aggregated records, not every raw item.
📊 Quick Math: If you process 1M reviews with Mistral Small 3.2 full analysis for $85, then generate 100 Claude Sonnet 4.6 theme summaries for $24, your total is about $109.
Cost scenario 1: Ecommerce brand with 50,000 reviews per month
An ecommerce brand collects reviews from its own store, Amazon, Walmart, TikTok Shop, and post-purchase surveys. The team wants weekly sentiment, product defect themes, and representative customer quotes.
Recommended workflow
- Full feedback analysis on all reviews
- Model: GPT-5 nano for clean short reviews
- Executive summary: GPT-5 mini
- Monthly volume: 50,000 reviews
- Token profile: 550 input / 100 output per item
Monthly cost
GPT-5 nano full analysis costs $0.675 per 10k items.
For 50k reviews:
- Full analysis: 5 × $0.675 = $3.38
- Weekly summaries: 5 batches × $0.023 with GPT-5 mini = $0.12
- Estimated total: $3.50 per month
This is the correct setup when reviews are short, repetitive, and easy to classify. The team should spend more effort on taxonomy design than model selection. A clean taxonomy with product area, complaint type, and urgency will produce more value than upgrading every record to a premium model.
When to upgrade
Upgrade the extraction model to GPT-5 mini when reviews are multilingual, sarcastic, highly domain-specific, or tied to legal/compliance workflows. At $337.50 per 1M full-analysis items, GPT-5 mini would process 50k reviews for about $16.88, still a small monthly cost.
Cost scenario 2: B2B SaaS company with 200,000 survey responses per month
A B2B SaaS company collects NPS comments, churn survey responses, onboarding feedback, support CSAT notes, and sales-loss notes. These responses are longer and more nuanced than ecommerce reviews.
Recommended workflow
- Long survey/support analysis on all responses
- Model: GPT-5 mini
- Executive summary: GPT-5
- Escalations: Claude Sonnet 4.6 for enterprise-account complaints
- Monthly volume: 200,000 responses
- Token profile: 900 input / 180 output per item
For the long-analysis profile, GPT-5 mini costs:
- Input: 9M tokens per 10k × $0.25 = $2.25
- Output: 1.8M tokens per 10k × $2.00 = $3.60
- Total: $5.85 per 10k items
For 200k responses:
- Full analysis: 20 × $5.85 = $117.00
- Executive summaries: 20 GPT-5 summary batches × $0.115 = $2.30
- Escalation review: 2,000 high-risk items with Claude Sonnet 4.6 at full-analysis profile = about $6.30
- Estimated total: $125.60 per month
This is the recommended setup for B2B because the cost of misclassifying enterprise feedback is higher than the API bill. GPT-5 mini is a strong default for structured extraction, while GPT-5 or Claude Sonnet 4.6 should produce the leadership narrative.
A premium-only approach using Claude Sonnet 4.6 for all 200k long responses would cost about:
- Claude Sonnet long-analysis per 10k: 9M × $3 + 1.8M × $15 = $54
- 200k items: 20 × $54 = $1,080
That is still affordable for some enterprises, but it is not necessary for every row. Use premium models on summaries, escalations, and disputed cases.
Cost scenario 3: Social listening team with 1 million posts per month
A social listening workflow monitors X, Reddit, TikTok comments, YouTube comments, forums, and public reviews. The goal is not perfect classification of every post. The goal is to detect brand risk, emerging complaints, competitor mentions, and spikes.
Recommended workflow
- Sentiment-only classification on all posts
- Model: Mistral Small 3.2
- Full analysis on high-signal subset: 5% of posts
- Clustering and summary: Claude Sonnet 4.6
- Monthly volume: 1,000,000 posts
Monthly cost
Sentiment-only with Mistral Small 3.2:
- $31 per 1M posts
Full analysis on top 5% high-signal posts:
- 50,000 posts × Mistral Small 3.2 full-analysis cost
- 5 × $0.85 = $4.25
Claude Sonnet 4.6 summaries for 100 batches:
- 100 × $0.240 = $24.00
Estimated total:
- Bulk sentiment: $31.00
- High-signal extraction: $4.25
- Executive summaries: $24.00
- Total: $59.25 per month
This architecture is ideal for social listening because most posts are low-value. You should classify everything cheaply, then spend summary budget only on clusters that matter.
✅ TL;DR: For high-volume social listening, never run premium extraction on every post. Classify all posts cheaply, enrich the top 5-10%, and summarize clusters with GPT-5 or Claude Sonnet.
Cost scenario 4: Support operations team with 300,000 tickets per month
Support feedback is more operational than social listening. The labels feed dashboards, escalation queues, help center priorities, and product bug reports. Accuracy matters because the output affects staffing and roadmap decisions.
Recommended workflow
- First-pass sentiment and urgency on all tickets
- Model: Gemini 2.0 Flash-Lite
- Full extraction on 20% of tickets
- Model: GPT-5 mini
- Executive summaries: Claude Sonnet 4.6
- Monthly volume: 300,000 tickets
Monthly cost
First-pass classification:
- Gemini 2.0 Flash-Lite sentiment-only: $25.50 per 1M
- 300k tickets: $7.65
Full extraction on top 20%:
- 60,000 tickets with GPT-5 mini full analysis
- GPT-5 mini full analysis: $3.375 per 10k
- 6 × $3.375 = $20.25
Executive summaries:
- 30 summary batches with Claude Sonnet 4.6
- 30 × $0.240 = $7.20
Estimated total:
- First pass: $7.65
- Detailed extraction: $20.25
- Summaries: $7.20
- Total: $35.10 per month
This is one of the best AI automation deals in customer operations. For less than the cost of a single SaaS seat, a support team can classify hundreds of thousands of tickets and produce weekly root-cause reports.
When to use each model tier
Model choice should follow the economic value of the decision being made.
Use budget models for bulk labeling
Choose GPT-5 nano, Gemini 2.0 Flash-Lite, Mistral Small 3.2, or DeepSeek V4 Flash when the task is:
- Basic sentiment classification
- Product review tagging
- Social listening first pass
- Language detection
- Spam or low-quality feedback filtering
- High-volume trend detection
Budget models are the right default when one bad label has low consequence and aggregate trends matter more than individual precision.
Use mid-tier models for structured extraction
Choose GPT-5 mini or Gemini 2.5 Flash when the workflow requires:
- Multiple fields in JSON
- Topic and subtopic extraction
- Urgency scoring
- Routing to teams
- Quote selection
- Survey analysis with nuanced responses
GPT-5 mini is the safest default for production sentiment systems because it is still inexpensive at $0.25 input / $2 output per 1M tokens and handles structured extraction reliably.
Use premium models for synthesis and escalations
Choose GPT-5, Claude Sonnet 4.6, or Claude Opus 4.7 when the output will be read by executives, customers, legal, or the board.
Premium models are best for:
- Monthly executive summaries
- Board-ready voice-of-customer reports
- Ambiguous complaint review
- Regulated industry feedback
- Enterprise account escalations
- Cross-theme synthesis
Do not use premium models for every row unless your volume is low or the feedback is high-stakes. The best ROI comes from using premium models after cheaper models have reduced and structured the data.
Per-10k and per-1M planning table
Use this table as a budgeting shortcut.
| Workflow | Model recommendation | Cost per 10k items | Cost per 1M items |
|---|---|---|---|
| Sentiment only, cheapest | GPT-5 nano | $0.23 | $23.00 |
| Sentiment only, budget alternative | DeepSeek V4 Flash | $0.39 | $39.20 |
| Sentiment + topics, low cost | Mistral Small 3.2 | $0.56 | $56.00 |
| Sentiment + topics, safer default | GPT-5 mini | $2.28 | $227.50 |
| Full feedback analysis, low cost | Mistral Small 3.2 | $0.85 | $85.00 |
| Full feedback analysis, default | GPT-5 mini | $3.38 | $337.50 |
| Long survey/support analysis | GPT-5 mini | $5.85 | $585.00 |
| Executive summary per 10k source items | GPT-5 | $0.115 | $11.50 |
| Executive summary per 10k source items | Claude Sonnet 4.6 | $0.240 | $24.00 |
For most teams, the winning architecture is:
- Reviews: GPT-5 nano or Mistral Small 3.2
- Surveys: GPT-5 mini
- Support tickets: Gemini 2.0 Flash-Lite first pass + GPT-5 mini enrichment
- Social listening: Mistral Small 3.2 first pass + Claude Sonnet summaries
- Executive reporting: GPT-5 or Claude Sonnet 4.6
If you want to test your own volume and token assumptions, run the numbers in AI Cost Check. For model-specific pricing, compare GPT-5 vs GPT-5 mini, GPT-5 vs DeepSeek V3.2, and Claude Opus 4.6 vs GPT-5 mini.
Cost optimization tactics for sentiment analysis
The fastest way to reduce sentiment analysis cost is not switching providers. It is reducing unnecessary output.
1. Use compact JSON
Ask for this:
{"s":"neg","c":0.91,"t":"billing","u":"high"}
Not this:
{
"sentiment": "The customer is expressing a negative sentiment because they are frustrated with a billing issue..."
}
Short keys and fixed labels reduce output tokens, improve parse reliability, and make downstream analytics easier.
2. Batch items by task and language
Batching reduces repeated prompt overhead. A 400-token system prompt sent once for 100 items costs far less than sending it 100 separate times. Group by language, source, and schema so the model can apply one instruction set consistently.
3. Separate classification from summaries
Run cheap classification on every item. Generate summaries from aggregated records, not raw text. This is why a 1M-item summary layer can cost only $11.50-$24 with GPT-5 or Claude Sonnet 4.6.
4. Route ambiguous items upward
Add a confidence threshold. For example:
- Confidence above 0.85: accept budget model output
- Confidence 0.60-0.85: reprocess with GPT-5 mini
- Confidence below 0.60 or high-risk category: send to Claude Sonnet 4.6 or human review
This gives premium-model quality where it matters without paying premium prices for obvious cases.
5. Limit quote extraction
Quote extraction increases output tokens. Extract quote candidates only for negative, high-confidence, or high-impact items. For social listening, selecting quotes from the top 5-10% of records is enough for executive reporting.
Recommended architecture for a production sentiment system
A production sentiment pipeline should have five stages.
Stage 1: Ingest and normalize text
Clean HTML, strip boilerplate, remove duplicate signatures, normalize emojis, preserve source metadata, and detect language. Do not send duplicate or empty text to the model.
Stage 2: First-pass classification
Use a budget model for sentiment, confidence, language, and basic category. This stage should be cheap enough to run on every item.
Recommended models:
- GPT-5 nano
- Gemini 2.0 Flash-Lite
- Mistral Small 3.2
- DeepSeek V4 Flash
Stage 3: Enrichment
Run richer extraction on records that need more detail: negative reviews, churn signals, enterprise accounts, bug reports, high-reach social posts, and low-confidence classifications.
Recommended models:
- GPT-5 mini
- Gemini 2.5 Flash
- Claude Haiku 4.5
Stage 4: Clustering and summarization
Aggregate by topic, sentiment, product area, week, customer segment, and source. Send only the compressed cluster data to a stronger model.
Recommended models:
- GPT-5
- Claude Sonnet 4.6
- Claude Opus 4.7 for premium reporting
Stage 5: Human review loop
Review sampled items weekly. Add corrections to your evaluation set. Update taxonomy when new themes emerge. Keep a holdout set so model changes do not silently degrade dashboards.
This architecture keeps monthly cost low while producing reliable outputs for operations, product, and leadership.
Frequently asked questions
How much does AI sentiment analysis cost in 2026?
AI sentiment analysis costs about $23-$39 per 1 million short items for sentiment-only classification on low-cost models like GPT-5 nano and DeepSeek V4 Flash. Full feedback analysis with sentiment, topics, urgency, and quote candidates costs about $85-$337.50 per 1 million items on practical production models.
What is the cheapest model for sentiment classification?
The cheapest listed option for sentiment classification is GPT-5 nano at $0.05 input / $0.40 output per 1M tokens, which works out to about $23 per 1M short sentiment-only items using the token profile in this guide. Gemini 2.0 Flash-Lite is close at about $25.50 per 1M items.
How much does it cost to analyze 10,000 reviews?
Basic sentiment analysis for 10,000 short reviews costs about $0.23 with GPT-5 nano, $0.31 with Mistral Small 3.2, and $0.39 with DeepSeek V4 Flash. Full feedback analysis with topics, urgency, and quote candidates costs about $0.85 with Mistral Small 3.2 or $3.38 with GPT-5 mini.
Which model should I use for survey analysis?
Use GPT-5 mini for survey analysis because survey responses are longer and more nuanced than star-rating reviews. A long survey/support profile costs about $5.85 per 10k responses or $585 per 1M responses with GPT-5 mini, making it a strong default for production workflows.
Should I use Claude or GPT-5 for every sentiment item?
No. Use budget or mid-tier models for per-item classification, then use GPT-5 or Claude Sonnet 4.6 for executive summaries, ambiguous escalations, and high-value accounts. This routing pattern can reduce costs by 10x-40x compared with running a premium model on every item.
Calculate your own sentiment analysis cost
The fastest way to budget your workflow is to estimate item volume, average input tokens, average output tokens, and model choice. Then compare several models side by side in AI Cost Check.
Recommended next steps:
- Use the AI Cost Check calculator for your exact monthly volume.
- Compare model pricing on GPT-5 vs GPT-5 mini.
- Review low-cost alternatives like DeepSeek V4 Flash, Mistral Small 3.2, and Gemini 2.0 Flash-Lite.
- Build your production stack with cheap classification, targeted enrichment, and premium summaries.