aifinhub

Comparator

Model Selector for Finance

Model selector finance: pick the right LLM for extract, summarize, forecast, compare, rank, synthesize — cost, latency, context, quality axes.

Inputs
Scenario form
Runtime
Instant
Privacy
Client-side · no upload
API key
Not required
Methodology
Open →

1 · Configure your task profile

Reference workload for cost fit: 6,000 in / 1,200 out × 3,000 calls/mo. See methodology.

2 · Top 3 recommendations

#1google

Gemini 2.5 Flash

haiku tier · 1M ctx

Cheapest frontier model in this table, with 1M context. Positioned for high-throughput pipelines. Reference monthly spend at this tool's default workload is ~$14, within the $50/mo budget. Published context window 1M covers the 32K–200K requirement. Vendor positions the Haiku tier for summarize workloads.

Vendor pricing →
#2anthropic

Claude Haiku 4.5

haiku tier · 200K ctx

Haiku-tier. Cheapest Anthropic rate, positioned for latency-sensitive filtering and extraction. Reference monthly spend at this tool's default workload is ~$36, within the $50/mo budget. Published context window 200K covers the 32K–200K requirement. Vendor positions the Haiku tier for summarize workloads.

Vendor pricing →
#3anthropic

Claude Sonnet 4.6

sonnet tier · 500K ctx · thinking

gate failed — see why-not

Sonnet-tier workhorse. 500K context and thinking-tokens at 1/5 of opus input rate. Reference monthly spend (~$108) exceeds the $50/mo budget at default workload. Published context window 500K covers the 32K–200K requirement. Vendor positions the Sonnet tier for summarize workloads.

Vendor pricing →

Published-rate-based; verify with your own eval harness (see D1 — Eval harness for finance LLMs).

3 · Full ranked list with why-not notes

#1Gemini 2.5 Flashgoogle · haiku
score 83

Passes all gates; simply outranked by a model with better combined fit.

#2Claude Haiku 4.5anthropic · haiku
score 76

Passes all gates; simply outranked by a model with better combined fit.

#3Claude Sonnet 4.6anthropic · sonnetgate failed
score 13

Over the chosen cost budget at default workload.

#4GPT-5 miniopenai · sonnetgate failed
score 13

Over the chosen cost budget at default workload.

#5o4-mini (reasoning)openai · sonnetgate failed
score 1

Over the chosen cost budget at default workload.

#6Claude Opus 4.7anthropic · opusgate failed
score 0

Over the chosen cost budget at default workload.

#7GPT-5openai · opusgate failed
score 0

Over the chosen cost budget at default workload.

#8Gemini 2.5 Progoogle · opusgate failed
score 0

Over the chosen cost budget at default workload.

4 · Per-axis comparison (all models)

ModelInput $/1MOutput $/1MContextThinkingRef $/moCostLatencyCtxCapability
Gemini 2.5 Flash$0.30$2.501M$14passpasspasspass
Claude Haiku 4.5$1.00$5.00200K$36passpasspasspass
Claude Sonnet 4.6$3.00$15.00500Kyes$108failpasspasspass
GPT-5 mini$2.00$8.00256K$65failpasspasspass
o4-mini (reasoning)$3.00$12.00200Kyes$97failpasspassfail
Claude Opus 4.7$15.00$75.001Myes$540failfailpassfail
GPT-5$10.00$40.00400Kyes$324failfailpassfail
Gemini 2.5 Pro$1.25$10.002Myes$58failfailpasspass

Hover cells for the axis note. Rates and context windows sourced from vendor pricing pages, as-of 2026-04-23.

Scoring framework

score = cost_match + latency_match + context_match
      + capability_bonus + quality_boost
cost_match    : 0 if monthly estimate > budget ceiling
latency_match : 0 if tier slower than latency budget
context_match : 0 if context window < required
capability    : bonus if task ∈ model.best_for
quality       : boost flagship tiers when quality = high

Deliberately no accuracy numbers. See methodology for why, and the framework article for deeper rationale.

Complementary tools

Planning estimates only — not financial, tax, or investment advice.