The short answer
On a short-context finance task (8k input, 500 output), 1,000 tasks cost $16.50 on Gemini 3.5 Flash, $41.70 on Claude Opus 4.7, and $55.00 on GPT-5.5, computed from the Token Cost Optimizer. Gemini 2.5 Flash-Lite does the same 1,000 tasks for $1.00, reframing the frontier comparison entirely.
On a short-context finance task (8k input, 500 output, the shape of a news-sentiment or tagging call), 1,000 tasks cost $16.50 on Gemini 3.5 Flash, $41.70 on Claude Opus 4.7, and $55.00 on GPT-5.5, all computed live from the Token Cost Optimizer. The budget tier reframes the whole comparison: Gemini 2.5 Flash-Lite does the same 1,000 tasks for $1.00. The frontier three are within a 3.3x band of each other and 16x to 55x above the budget floor.
TL;DR
| Model | Cost per 1,000 tasks |
|---|---|
| Gemini 2.5 Flash-Lite | $1.00 |
| Gemini 2.5 Flash | $3.65 |
| Claude Haiku 4.5 | $8.34 |
| Gemini 3.5 Flash | $16.50 |
| Claude Opus 4.7 | $41.70 |
| GPT-5.5 | $55.00 |
Same task for every row: 8,000 input + 500 output tokens, one call per task, no retry, a 0.30 cache-hit assumption, priced as 1,000 tasks in a day. Anthropic input reflects the 30% cache hit; Google and OpenAI are priced at full list input.
What a "task" is here
A task is a single short-context LLM call: classify the sentiment of a news item, tag a filing paragraph, score a headline, extract one field. It is the atomic unit of a high-volume finance pipeline. The 8k-input, 500-output shape is typical: a news item plus an instruction in, a structured score out. Cost-per-1,000-tasks is the unit that scales, because these pipelines run tens of thousands of tasks a day.
The three frontier models, head to head
Among the three frontier headline models, the ranking on cost-per-1,000-tasks is clear and not close to a tie:
- Gemini 3.5 Flash $16.50. The cheapest frontier option, by a wide margin.
- Claude Opus 4.7 $41.70. About 2.5x Gemini 3.5 Flash.
- GPT-5.5 $55.00. About 3.3x Gemini 3.5 Flash.
The driver is the rate table. Gemini 3.5 Flash bills $1.50/$9.00 per Mtok1; GPT-5.5 bills $5.00/$30.002. On a 500-token output the output rate matters, and GPT-5.5's $30/Mtok output is what pushes it to the top of the three. Opus 4.7, at $5/$253, lands between them, helped slightly by the 30% input cache that only Anthropic gets in this engine.
At 50,000 tasks a day, the band translates to real money: $825/day on Gemini 3.5 Flash, $2,085/day on Opus 4.7, $2,750/day on GPT-5.5. Over a month that is roughly $25k versus $63k versus $83k for the identical 1.5 million tasks.
The budget tier changes the question
The frontier three-way is interesting, but the dominant fact is the budget floor. Gemini 2.5 Flash-Lite does 1,000 tasks for $1.00, 16x cheaper than Gemini 3.5 Flash and 55x cheaper than GPT-5.5. Claude Haiku 4.5 ($8.34) and Gemini 2.5 Flash ($3.65) sit in between.
For sentiment scoring and tagging, the task is structural classification at volume, exactly the regime where a budget model, ideally one fine-tuned on your label set, captures most of the value. A frontier model on a sentiment call is paying reasoning rates for a classification job. The defensible architecture is a budget first pass on all 1,000 tasks, with frontier escalation only on the ambiguous subset the cheap model flags. Routing 10% of tasks to Gemini 3.5 Flash on top of a Flash-Lite base costs about $2.65 per 1,000, versus $16.50 to run everything on the frontier model.
A capability caveat the cost numbers cannot settle
Cost is not quality. These figures say nothing about which model classifies a sentiment correctly, follows a structured-output schema reliably, or handles a sarcastic headline. Vendors publish capability benchmarks; treat them as vendor claims and run your own eval on a labeled sample of your real tasks. The cost ranking is firm; the accuracy ranking is yours to establish. The right model is the cheapest one that clears your accuracy bar, and on a structural classification task that is very often a budget model.
Decision guidance
- Price in cost-per-1,000-tasks, not per-call. A fraction of a cent per call becomes tens of thousands of dollars a month at volume; the per-1,000 unit makes the decision legible.
- Default to the budget tier for classification. Sentiment, tagging, and field extraction are structural; the 16x-to-55x frontier premium rarely earns out.
- Two-stage on ambiguity. Cheap first pass, frontier escalation on flagged items, beats running everything on the frontier model.
- Recompute your real token shape. A longer instruction or a richer structured output moves the per-1,000 cost; the Token Cost Optimizer recomputes it instantly.
Connects to
- Token Cost Optimizer: the engine behind every per-1,000 figure here. Recompute with your own task shape.
- The LLM-in-Finance Economics Report 2026: the full four-workload report this spoke feeds into.
- Cheapest LLM for SEC 10-K Extraction at 10,000 Filings a Month 2026: the at-scale extraction spoke.
- Gemini 3.5 Flash vs GPT-5.5 vs Claude Opus 4.7 for Finance Extraction 2026: the same three models on a long-context extraction job.
References
Footnotes
-
Google. "Gemini Developer API pricing." ai.google.dev, verified 2026-05-26. https://ai.google.dev/gemini-api/docs/pricing ↩
-
OpenAI. "API Pricing." developers.openai.com, verified 2026-05-26. https://developers.openai.com/api/docs/pricing ↩
-
Anthropic. "Pricing." platform.claude.com, verified 2026-05-26. https://platform.claude.com/docs/en/about-claude/pricing ↩
Verified engine output
Show the recompute-verified inputs and outputs
| input_tokens_per_call | 8000 |
|---|---|
| output_tokens_per_call | 500 |
| calls_per_idea | 1 |
| retry_rate | 0 |
| ideas_per_day | 1000 |
| validation_rate | 0.9 |
| cache_hit_rate | 0.3 |
| model_id | gemini-2-5-flash-lite |
| model › id | gemini-2-5-flash-lite |
|---|---|
| model › provider | |
| model › name | Gemini 2.5 Flash-Lite |
| model › input usd per mtoken | 0.1 |
| model › output usd per mtoken | 0.4 |
| model › context window | 1000000 |
| model › notes | Cheapest tier in this table; 1M context. |
| effective cost per call | 0.001 |
| cost per idea | 0.001 |
| cost per validated trade | 0.0011111111111111111 |
| cost per day | 1 |
| cost per month | 30 |
| cost per year | 365 |
Computed live at build time.
| input_tokens_per_call | 8000 |
|---|---|
| output_tokens_per_call | 500 |
| calls_per_idea | 1 |
| retry_rate | 0 |
| ideas_per_day | 1000 |
| validation_rate | 0.9 |
| cache_hit_rate | 0.3 |
| model_id | gemini-2-5-flash |
| model › id | gemini-2-5-flash |
|---|---|
| model › provider | |
| model › name | Gemini 2.5 Flash |
| model › input usd per mtoken | 0.3 |
| model › output usd per mtoken | 2.5 |
| model › context window | 1000000 |
| model › notes | Fast mid-tier; 1M context. |
| effective cost per call | 0.0036499999999999996 |
| cost per idea | 0.0036499999999999996 |
| cost per validated trade | 0.004055555555555555 |
| cost per day | 3.6499999999999995 |
| cost per month | 109.49999999999999 |
| cost per year | 1332.2499999999998 |
Computed live at build time.
| input_tokens_per_call | 8000 |
|---|---|
| output_tokens_per_call | 500 |
| calls_per_idea | 1 |
| retry_rate | 0 |
| ideas_per_day | 1000 |
| validation_rate | 0.9 |
| cache_hit_rate | 0.3 |
| model_id | claude-haiku-4-5 |
| model › id | claude-haiku-4-5 |
|---|---|
| model › provider | anthropic |
| model › name | Claude Haiku 4.5 |
| model › input usd per mtoken | 1 |
| model › output usd per mtoken | 5 |
| model › cache write usd per mtoken | 1.25 |
| model › cache read usd per mtoken | 0.1 |
| model › context window | 200000 |
| model › notes | Fast, cheap — filtering + pre-processing layers. |
| effective cost per call | 0.00834 |
| cost per idea | 0.00834 |
| cost per validated trade | 0.009266666666666666 |
| cost per day | 8.34 |
| cost per month | 250.2 |
| cost per year | 3044.1 |
Computed live at build time.
| input_tokens_per_call | 8000 |
|---|---|
| output_tokens_per_call | 500 |
| calls_per_idea | 1 |
| retry_rate | 0 |
| ideas_per_day | 1000 |
| validation_rate | 0.9 |
| cache_hit_rate | 0.3 |
| model_id | gemini-3-5-flash |
| model › id | gemini-3-5-flash |
|---|---|
| model › provider | |
| model › name | Gemini 3.5 Flash |
| model › input usd per mtoken | 1.5 |
| model › output usd per mtoken | 9 |
| model › context window | 1000000 |
| model › notes | Frontier agent-tier at Flash speed — not a budget model (output ~3.6x Gemini 2.5 Flash). |
| effective cost per call | 0.0165 |
| cost per idea | 0.0165 |
| cost per validated trade | 0.018333333333333333 |
| cost per day | 16.5 |
| cost per month | 495 |
| cost per year | 6022.5 |
Computed live at build time.
| input_tokens_per_call | 8000 |
|---|---|
| output_tokens_per_call | 500 |
| calls_per_idea | 1 |
| retry_rate | 0 |
| ideas_per_day | 1000 |
| validation_rate | 0.9 |
| cache_hit_rate | 0.3 |
| model_id | claude-opus-4-7 |
| model › id | claude-opus-4-7 |
|---|---|
| model › provider | anthropic |
| model › name | Claude Opus 4.7 |
| model › input usd per mtoken | 5 |
| model › output usd per mtoken | 25 |
| model › cache write usd per mtoken | 6.25 |
| model › cache read usd per mtoken | 0.5 |
| model › context window | 1000000 |
| model › notes | Flagship reasoning model — 1M context. |
| effective cost per call | 0.0417 |
| cost per idea | 0.0417 |
| cost per validated trade | 0.04633333333333333 |
| cost per day | 41.7 |
| cost per month | 1251 |
| cost per year | 15220.500000000002 |
Computed live at build time.
| input_tokens_per_call | 8000 |
|---|---|
| output_tokens_per_call | 500 |
| calls_per_idea | 1 |
| retry_rate | 0 |
| ideas_per_day | 1000 |
| validation_rate | 0.9 |
| cache_hit_rate | 0.3 |
| model_id | gpt-5 |
| model › id | gpt-5 |
|---|---|
| model › provider | openai |
| model › name | GPT-5.5 |
| model › input usd per mtoken | 5 |
| model › output usd per mtoken | 30 |
| model › context window | 400000 |
| model › notes | OpenAI frontier model (GPT-5.5). |
| effective cost per call | 0.055 |
| cost per idea | 0.055 |
| cost per validated trade | 0.06111111111111111 |
| cost per day | 55 |
| cost per month | 1650 |
| cost per year | 20075 |
Computed live at build time.
Frequently asked questions
- What is the cost per 1,000 tasks for Gemini 3.5 Flash vs Opus 4.7 vs GPT-5.5?
- On an 8k-input, 500-output finance task: Gemini 3.5 Flash $16.50, Claude Opus 4.7 $41.70, and GPT-5.5 $55.00 per 1,000 tasks, all computed from the Token Cost Optimizer. Gemini 3.5 Flash is the cheapest of the three frontier models by a wide margin.
- Which is the cheapest model for high-volume finance classification?
- Gemini 2.5 Flash-Lite at $1.00 per 1,000 tasks, 16x cheaper than Gemini 3.5 Flash and 55x cheaper than GPT-5.5. Sentiment scoring and tagging are structural classification, where the budget tier captures most of the value at a fraction of the frontier cost.
- Why is GPT-5.5 the most expensive of the three frontier models per task?
- Its output rate. GPT-5.5 bills $30/Mtok output against Gemini 3.5 Flash's $9/Mtok, and on a 500-token output that gap drives GPT-5.5 to $55.00 per 1,000 tasks versus Gemini 3.5 Flash's $16.50, a 3.3x difference.
- How much does running 50,000 finance tasks a day cost on each model?
- Scaling the per-1,000 figures: about $825/day on Gemini 3.5 Flash, $2,085/day on Claude Opus 4.7, and $2,750/day on GPT-5.5. Over a month that is roughly $25k, $63k, and $83k for the same 1.5 million tasks.
- Are these cost numbers or accuracy scores?
- Cost numbers only, computed from verified vendor list prices and recomputed by CI against the shipped cost engine. They say nothing about which model classifies correctly. Run your own eval on a labeled sample, then pick the cheapest model that clears your accuracy bar.