The short answer
There is no single best LLM for financial analysis in 2026; the right model is task-tiered. For high-volume extraction use Gemini 2.5 Flash ($0.30/$2.50 per Mtok) or Claude Haiku 4.5 ($1/$5). For long-context synthesis, Gemini 2.5 Pro ($1.25/$10, 2M context). For the hardest reasoning, Claude Opus 4.7 ($5/$25) or GPT-5.5 ($5/$30).
There is no single best LLM for financial analysis in 2026; the right model is task-tiered. For high-volume extraction (parsing filings, tagging news), use a cheap model: Gemini 2.5 Flash ($0.30/$2.50 per Mtoken) or Claude Haiku 4.5 ($1/$5). For long-context whole-filing synthesis, Gemini 2.5 Pro ($1.25/$10, 2M context) is the value pick. For the hardest reasoning, Claude Opus 4.7 ($5/$25) or GPT-5.5 ($5/$30). Match the model to the task with the Model Selector for Finance.
TL;DR
| Model | Input $/Mtok | Output $/Mtok | Context | Tier role |
|---|---|---|---|---|
| Claude Opus 4.7 | $5 | $25 | 1M | Hardest reasoning |
| Claude Sonnet 4.6 | $3 | $15 | 1M | Production workhorse |
| Claude Haiku 4.5 | $1 | $5 | 200K | Extraction / filtering |
| GPT-5.5 | $5 | $30 | 400K | Frontier reasoning |
| GPT-5.4-mini | $0.75 | $4.50 | (mid-tier) | Cheap reasoning |
| Gemini 3.5 Flash | $1.50 | $9 | 1M | Google's current frontier (Flash latency) |
| Gemini 2.5 Pro | $1.25 | $10 | 2M | Long-context value (2M window) |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | Cheapest extraction |
| DeepSeek V4 Flash | $0.14 | $0.28 | 1M | Budget open-weight |
All list prices verified 2026-05-25 against each vendor's official pricing page (Anthropic, OpenAI, Google, DeepSeek). Gemini 2.5 Pro input rises to $2.50/Mtok above 200K input tokens.
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. Verified 2026-05-25:
- Gemini 2.5 Flash — $0.30 in / $2.50 out, 1M context. The cheapest frontier-family model with a large window; ideal for whole-filing extraction in one pass.
- Claude Haiku 4.5 — $1 in / $5 out, 200K context. Fast, with the strongest prompt-cache economics (cache reads at $0.10/Mtok) for repeated filing boilerplate.
- DeepSeek V4 Flash: $0.14 in / $0.28 out, 1M context. The budget-open-weight floor when latency and provider-trust constraints allow it.
At these rates, processing a 120K-token filing costs cents, not dollars. The cost-per-filing math is in Cheapest LLM for SEC Filings 2026.
The reasoning tier: pay for the frontier
For the high-stakes reasoning step, model the cost as small (low volume) and buy the strongest tier. Verified 2026-05-25:
- Claude Opus 4.7 — $5 in / $25 out, 1M context, thinking-tokens support. Anthropic's flagship; note the price dropped from the prior $15/$75 Opus generation.
- GPT-5.5 — $5 in / $30 out, 400K context API window, reasoning support. OpenAI's frontier; prompts above 272K input tokens are priced at 2x input / 1.5x output.
- Claude Sonnet 4.6 — $3 in / $15 out, 1M context. The production-workhorse middle ground when Opus/GPT-5.5 is overkill but Haiku is too light.
- Gemini 3.5 Flash — $1.50 in / $9 out, 1M context. Google's current frontier (launched May 19, 2026), positioned for agent-tier reasoning at Flash latency. Priced like a frontier model, not the economy tier, so reserve it for steps that genuinely need the judgment.
The long-context tier: Gemini 2.5 Pro on value
For answering over a whole filing or a batch of transcripts, the deciding axes are context window and $/token at scale:
- Gemini 2.5 Pro — $1.25 in / $10 out, 2M context (input $2.50/Mtok above 200K). The largest window in this table and the lowest frontier input rate; the value pick for document-heavy synthesis.
A 2M-token window means a multi-document corpus fits in one call without chunking; the low input rate keeps a 500K-token synthesis affordable.
The decision, computed live
The Model Selector for Finance ranks models on task-fit across cost, latency, context, and capability gates. The scenario below asks for long-context synthesis at high quality with a 200K-1M context need and a sub-30s latency budget; the engine ranks Gemini 2.5 Pro first on combined fit. The verified output block at the foot of the page is computed live from the shipped engine bundle.
The engine's embedded price snapshot matches the verified rates above. On this scenario it ranks Gemini 2.5 Pro first ($58/mo), Gemini 3.5 Flash second ($59/mo), and Claude Opus 4.7 third ($180/mo); Claude Sonnet 4.6 ($108/mo) follows in fourth, all clearing the cost gate. GPT-5.5 (~$198/mo) is the one frontier-tier model the scenario disqualifies, and on context rather than cost: its 400K window is below the 1M the scenario requires. Both the per-model prices in the table above and the engine output share the same verified rate table.
Decision guidance
- High-volume extraction / news tagging: Gemini 2.5 Flash or Claude Haiku 4.5; DeepSeek V4 Flash for the budget floor.
- Whole-filing or multi-document synthesis: Gemini 2.5 Pro (2M context, low input rate).
- Hardest reasoning / forecasting: Claude Opus 4.7 or GPT-5.5.
- Balanced production default: Claude Sonnet 4.6.
- Repeated boilerplate (filing structure, system prompts): layer prompt caching on top; see OpenAI Prompt Caching Pricing 2026.
Related in this series
- Claude vs GPT-5 vs Gemini for Financial Analysis 2026: the three-way frontier head-to-head.
- Cheapest LLM for SEC Filings 2026: the $/filing extraction math.
- OpenAI Prompt Caching Pricing 2026: the caching lever.
- Model Selection Framework for Finance: the methodology behind tiering.
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.
References
- Anthropic. "Pricing." platform.claude.com/docs/en/about-claude/pricing, verified 2026-05-25 (Opus 4.7 $5/$25, Sonnet 4.6 $3/$15, Haiku 4.5 $1/$5; 1M/200K context).
- OpenAI. "API Pricing." developers.openai.com/api/docs/pricing, verified 2026-05-25 (GPT-5.5 $5/$30, GPT-5.4-mini $0.75/$4.50; >272K input priced 2x/1.5x).
- Google. "Gemini API Pricing." ai.google.dev/gemini-api/docs/pricing, verified 2026-05-25 (2.5 Pro $1.25/$10 to 200K, $2.50/$15 above; 2.5 Flash $0.30/$2.50).
- DeepSeek. "Models & Pricing." api-docs.deepseek.com/quick_start/pricing, verified 2026-05-25 (V4 Flash $0.14 cache-miss in / $0.28 out, 1M context).
Verified engine output
Show the recompute-verified inputs and outputs
| task | synthesize |
|---|---|
| latency | sub_30s |
| cost | b200 |
| context | k200_1m |
| quality | high |
| ranked (10 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 Gemini 2.5 Flash or Claude Haiku 4.5 for extraction, Gemini 2.5 Pro for long-context synthesis, and Claude Opus 4.7 or GPT-5.5 for the hardest reasoning (prices verified 2026-05-25).
- Which LLM is cheapest for finance work?
- For hosted frontier-family models, Gemini 2.5 Flash at $0.30/$2.50 per Mtoken; DeepSeek V4 Flash is cheaper still at $0.14/$0.28 if its provider profile fits your constraints.
- Which model has the largest context for whole-filing analysis?
- Gemini 2.5 Pro at 2M tokens, followed by Claude Opus 4.7 and Sonnet 4.6 at 1M each; GPT-5.5's API window is 400K.
- 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.