The short answer
There is no single best LLM for financial analysis; the right model is task-tiered. For high-volume extraction use the budget tier (Claude Haiku 5.5, GPT-6 Luna, Gemini 3.5 Flash-Lite). For long-context synthesis, Gemini 3.8 Flash. For the hardest reasoning, Gemini 4 Argon ($2/$10 per Mtok) or Gemini 3.1 Pro Preview ($2/$12 per Mtok).
There is no single best LLM for financial analysis; the right model is task-tiered. For high-volume extraction (parsing filings, tagging news) use the budget tier, where Claude Haiku 5.5 is the cheapest current model at about $0.013 for a full-filing pass. For whole-filing synthesis, Gemini 3.8 Flash has the lowest input rate among the 1M-context models outside the budget tier. For the hardest reasoning, the premium tier: Gemini 4 Argon ($2 in, $10 out per Mtok) or Gemini 3.1 Pro Preview ($2 in, $12 out per Mtok).
TL;DR
| Model | Input $/Mtok | Output $/Mtok | Context | ~$ per 10-K pass (120K in, 3K out) | Tier role |
|---|---|---|---|---|---|
| Claude Haiku 5.5 | $0.10 | $0.50 | 1M | $0.013 | Extraction / filtering |
| GPT-6 Luna | $0.10 | $0.50 | 1.1M | $0.013 | Extraction / filtering |
| Gemini 3.5 Flash-Lite | $0.30 | $2.50 | 1M | $0.043 | Extraction / filtering |
| Gemini 3.8 Flash | $0.75 | $3.75 | 1M | $0.101 | Production workhorse |
| GPT-5.4 Mini | $0.75 | $4.50 | 400K | $0.103 | Production workhorse |
| Claude Sonnet 5.5 | $2 | $10 | 1M | $0.270 | Production workhorse |
| GPT-6.1 Sol | $2 | $10 | 1.1M | $0.270 | Production workhorse |
| Gemini 4 Argon | $2 | $10 | 1M | $0.270 | Hardest reasoning |
| Gemini 3.1 Pro Preview | $2 | $12 | 1M | $0.276 | Hardest reasoning |
| Claude Opus 5.5 | $4 | $20 | 1M | $0.540 | Hardest reasoning |
| GPT-5.5 | $5 | $30 | 1.1M | $0.690 | Hardest reasoning |
| Claude Fable 5.1 | $10 | $50 | 1M | $1.350 | Hardest reasoning |
| GPT-6 Astra | $10 | $50 | 1.1M | $1.350 | Hardest reasoning |
| DeepSeek V4.1-Flash | $0.30 | $1.20 | 1M | $0.040 peak, $0.020 off-peak | Budget floor |
Catalog models: list prices from the aidevhub.io model catalog, each row verified on the vendor's pricing page (catalog prices verified 2026-10-09). DeepSeek: api-docs.deepseek.com, verified 2026-09-28 (peak rates; off-peak is half). Per-pass costs are list price × tokens, not a benchmark run.
Why "best LLM for finance" is the wrong question
Financial analysis is not one task. It is at least three, each with a different cost/quality frontier:
- Extraction: pull numbers and entities from filings, tag news sentiment, normalize tables. High volume, low reasoning. Optimize for $/token, not peak intelligence.
- Reasoning: weigh evidence, reconcile conflicting signals, forecast. Lower volume, high stakes. Pay for the frontier tier here.
- Long-context synthesis: answer questions over a whole 10-K (100K+ tokens) or a quarter of transcripts. Optimize for context window and $/token at scale.
Picking one model for all three overpays on extraction and underpowers reasoning. The disciplined move is a tiered stack: cheap model for extraction, frontier model for the reasoning step, large-context model for whole-document synthesis.
The extraction tier: cheapest wins
For parsing filings and tagging news at volume, the cheapest capable model wins because the task is mechanical:
- Claude Haiku 5.5 (Anthropic): $0.10 in / $0.50 out per Mtok, 1M context, about $0.013 per full-filing pass (prompts up to 100K tokens; 5-minute cache writes).
- GPT-6 Luna (OpenAI): $0.10 in / $0.50 out per Mtok, 1.1M context, about $0.013 per full-filing pass (prompts up to 272K input tokens).
- Gemini 3.5 Flash-Lite (Google): $0.30 in / $2.50 out per Mtok, 1M context, about $0.043 per full-filing pass.
- DeepSeek V4.1-Flash (DeepSeek): $0.30 in / $1.20 out at peak, half that off-peak, 1M context, about $0.040 per pass at peak and $0.020 off-peak. DeepSeek V4 for Finance 2026 covers where it holds up on financial tasks and where provider constraints rule it out.
At these rates a 120K-token filing costs cents, not dollars. The cost-per-filing math at desk volume is in Claude vs GPT vs Gemini for Financial Analysis.
The production workhorse tier
When the premium tier is overkill but the budget tier is too light:
- Gemini 3.8 Flash (Google): $0.75 in / $3.75 out per Mtok, 1M context, about $0.101 per full-filing pass (promotional price through December 31, 2026).
- GPT-5.4 Mini (OpenAI): $0.75 in / $4.50 out per Mtok, 400K context, about $0.103 per full-filing pass.
- Claude Sonnet 5.5 (Anthropic): $2 in / $10 out per Mtok, 1M context, about $0.270 per full-filing pass (5-minute cache writes).
- GPT-6.1 Sol (OpenAI): $2 in / $10 out per Mtok, 1.1M context, about $0.270 per full-filing pass (prompts up to 272K input tokens).
The reasoning tier: pay for the frontier
For the high-stakes reasoning step, the volume is low, so buy the strongest tier:
- Gemini 4 Argon (Google): $2 in / $10 out per Mtok, 1M context, about $0.270 per full-filing pass (promotional price through November 30, 2026).
- Gemini 3.1 Pro Preview (Google): $2 in / $12 out per Mtok, 1M context, about $0.276 per full-filing pass (prompts up to 200K tokens).
- Claude Opus 5.5 (Anthropic): $4 in / $20 out per Mtok, 1M context, about $0.540 per full-filing pass (5-minute cache writes).
- GPT-5.5 (OpenAI): $5 in / $30 out per Mtok, 1.1M context, about $0.690 per full-filing pass (prompts up to 272K input tokens).
- Claude Fable 5.1 (Anthropic): $10 in / $50 out per Mtok, 1M context, about $1.350 per full-filing pass (5-minute cache writes).
- GPT-6 Astra (OpenAI): $10 in / $50 out per Mtok, 1.1M context, about $1.350 per full-filing pass (prompts up to 272K input tokens).
Claude vs GPT vs Gemini for Financial Analysis 2026 compares the tiers vendor by vendor.
The long-context tier
For answering over a whole filing or a batch of transcripts, the deciding axes are context window and input rate at scale. Among the 1M-context models outside the budget tier, Gemini 3.8 Flash has the lowest input rate ($0.75 per Mtok, promotional price through December 31, 2026). A large window is not the same as accuracy at depth: the long-context financial QA benchmark 2026 measures how well models actually answer over a full-window financial corpus.
The decision, computed live
The Model Selector for Finance ranks models on task fit across cost, latency, context and capability gates, pricing from the same catalog as the table above. The scenario below asks for long-context synthesis at high quality with a 200K-1M context need, a sub-30s latency budget and a $200/month reference budget. At build it ranks first Gemini 4 Argon ($72/mo), second Gemini 3.1 Pro Preview ($79/mo), third Gemini 3.8 Flash (~$27/mo). The verified output block at the foot of the page is the engine's own result.
Decision guidance
- High-volume extraction / news tagging: Claude Haiku 5.5 or GPT-6 Luna or Gemini 3.5 Flash-Lite; DeepSeek V4.1-Flash for the budget floor when its provider profile fits.
- Whole-filing or multi-document synthesis: Gemini 3.8 Flash, the lowest input rate at 1M context outside the budget tier.
- Hardest reasoning / forecasting: Gemini 4 Argon or Gemini 3.1 Pro Preview.
- Balanced production default: Gemini 3.8 Flash.
- Repeated boilerplate (filing structure, system prompts): layer prompt caching on top; see How to Set Up Prompt Caching for Finance.
Related in this series
- Claude vs GPT vs Gemini for Financial Analysis 2026: the three-way head-to-head by tier.
- Model Selector for Finance: ranks the current lineup for your task, latency and budget.
Connects to
- Model Selector for Finance: the engine behind this page's ranking.
- Token Cost Optimizer: the $/workload calculator.
- Reading Financial Filings with LLMs 2026: the filings-analysis pipeline.
Sources
Model prices are the vendors' list prices, read from the pages below and kept in the model price catalog the cost tools share.
- Anthropic, platform.claude.com/docs/en/about-claude/models/overview: Claude Fable 5.1, Claude Opus 5.5, Claude Sonnet 5.5 (read 2026-10-16).
- Anthropic, platform.claude.com/docs/en/about-claude/pricing: Claude Haiku 5.5 (read 2026-10-16).
- Google, ai.google.dev/gemini-api/docs/pricing: Gemini 4 Argon, Gemini 3.8 Flash, Gemini 3.5 Flash-Lite, Gemini 3.1 Pro Preview (read 2026-10-18).
- OpenAI, developers.openai.com/api/docs/pricing: GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna (read 2026-10-16).
- OpenAI, developers.openai.com/api/docs/models/gpt-5.5: GPT-5.5 (read 2026-10-16).
- OpenAI, developers.openai.com/api/docs/models/gpt-5.4-mini: GPT-5.4 Mini (read 2026-10-16).
- DeepSeek, api-docs.deepseek.com/quick_start/pricing: DeepSeek V4.1-Flash (read 2026-09-28).
Verified engine output
Show the recompute-verified inputs and outputs
| task | synthesize |
|---|---|
| latency | sub_30s |
| cost | b200 |
| context | k200_1m |
| quality | high |
| ranked (13 items) | [...] |
|---|
Computed live at build time.
Frequently asked questions
- What is the best LLM for financial analysis in 2026?
- It is task-tiered, not one model. Use the budget tier (Claude Haiku 5.5, GPT-6 Luna, Gemini 3.5 Flash-Lite) for extraction, Gemini 3.8 Flash for long-context synthesis, and Gemini 4 Argon or Gemini 3.1 Pro Preview for the hardest reasoning. Prices from the vendor-verified model catalog (verified 2026-10-09).
- Which LLM is cheapest for finance work?
- Among the current Claude, GPT and Gemini lineups, Claude Haiku 5.5 at $0.1/$0.5 per Mtok, about $0.013 for a 120K-in, 3K-out filing pass. DeepSeek V4.1-Flash is $0.3/$1.2 at peak and half that off-peak (about $0.040 and $0.020 per pass), provided its provider profile fits your constraints.
- Which model has the largest context for whole-filing analysis?
- Claude Haiku 5.5, GPT-6 Luna, Gemini 3.5 Flash-Lite, Gemini 3.8 Flash, Claude Sonnet 5.5, GPT-6.1 Sol, Gemini 4 Argon, Gemini 3.1 Pro Preview, Claude Opus 5.5, GPT-5.5, Claude Fable 5.1, GPT-6 Astra all offer 1M-token or larger context windows; outside the budget tier, Gemini 3.8 Flash has the lowest input rate at $0.75 per Mtok.
- Should I use one model or several for financial analysis?
- Several. A tiered stack (cheap extraction, frontier reasoning, large-context synthesis) is cheaper and stronger than forcing one model across every task.