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Long-context agent workflows

Need 200K–1M context for large docs, codebases, or multi-hour agent state.

At a glance — Oct 2026

Updated 2026-10-27 · forward this page: /jtbds/long-context-agents#brief

Scores as of 2026-10-19 (8 days old).

Depot Pick
Google AI (Gemini) · 8.43/10 for this job (curated estimate, high confidence)

Capability includes context window + needle quality. Gemini often wins value at 1M; Claude strong at high quality long context.

Runner-up
Anthropic · 8.42/10

Scores higher than the pick on: Capability fit, Latency / reliability, Developer & agent DX, Privacy / data retention.

When not to use the pick
Quality on tool use matters more than raw context — then Anthropic; enterprise GCP already → Vertex; want Kimi K3 1M ctx at $3/$15 — moonshot.
Lock-in / migration
The pick has lower stored lock-in risk than runner-up Anthropic (6 vs 6.5). Self-host fallback listed for this job: OpenAI MRCR.
Sourced price (usd_per_million_input_tokens)

Pick's lowest sourced SKU: gemini-3.5-flash-lite $0.3 (usd_per_million_input_tokens) — source, as of 2026-10-21

Cheapest matching SKU: Anthropic Haiku 5.5 (prompts up to 100K tokens) $0.1 — source, as of 2026-10-27, re-checked 2026-10-27. Not the pick; not quality-adjusted.

Price history: first seen 2026-10-21 · last changed no change recorded · history JSON

Free option
  • OpenAI MRCR (oss) — source, as of 2026-10-25
Failure modes to watch
  • Context overflow: If the input exceeds the window, say so and retrieve a narrower slice. Do not answer from a truncated head as if it were the whole source.
  • Instruction drift: Restate the stop condition in the trace. End the run when it is met. Review long traces for steps that do not serve the original task.
  • Lost in the middle: Ask the agent to name the passage it used. Spot-check questions whose only support sits in the middle of the context, not only at the edges.
  • Runaway spend: Set a budget and a tool-call cap per task. Alert when spend jumps. Stop the run at the cap instead of summarizing a partial trace as success.
  • Silent truncation: Tools must return whether the payload was truncated and how long the original was. Fail the task if the answer depends on the missing span.
Reported outcomes (submitted reports only)

This job: loading… JSON · reports never change scores · seed/test records excluded

Depot Pick (Google AI (Gemini)) on this job: loading… JSON · reports never change scores · seed/test records excluded

Same-job, same-unit comparisons: Google AI (Gemini) vs Anthropic · Google AI (Gemini) vs OpenAI · Google AI (Gemini) vs Moonshot (Kimi)

Cite: Agent Provider Depot, Depot Pick for long-context-agents: google-ai, scores as of 2026-10-19. Agents: GET /api/decide?jtbd=long-context-agents

Free Long-context agent workflows options · All sourced prices, cheapest first

Depot Pick

Google AI (Gemini)

Capability includes context window + needle quality. Gemini often wins value at 1M; Claude strong at high quality long context.

Pricing (directional): Gemini 3.x Pro often ~$2/$12 under 200K; Flash tiers much cheaper. 1M context. Vertex for enterprise. Batch half-price common.

Cite: Depot Pick for long-context-agents: google-ai
Overall
8.4
curated_estimate
confidence: high · as of 2026-10-19
Capability fit
9.2
Price / performance
8.8
Latency / reliability
8.5
Developer & agent DX
8.2
Privacy / data retention
8.0
Lock-in risk (higher = worse)
6.0
Maturity / track record
9.6

Lock-in risk: higher = more lock-in (worse). Overall already inverts this dimension.

Sources: Google AI for Developers: https://ai.google.dev · Gemini API: https://ai.google.dev/gemini-api

Best value frontier stack and multimodal king. Use Flash for high-volume agent loops; Pro when quality matters. Prefer Vertex for enterprise compliance.

Quote for Long-context agent workflows

Depot Pick stays Google AI (Gemini). Prices below are sourced SKU rows only (as of 2026-10-27). Input and output are not blended. GET /api/quote?jtbd=long-context-agents

Pick is not a price winner. google-ai sourced input $/1M is 0.3; cheapest among candidates is anthropic at 0.1.

  • gemini-2.5-pro usd_per_million_input_tokens $2.5 source as of 2026-10-03
  • gemini-2.5-pro usd_per_million_input_tokens $1.25 source as of 2026-10-03
  • gemini-3-flash-preview usd_per_million_input_tokens $1 source as of 2026-10-03
  • gemini-3-flash-preview usd_per_million_input_tokens $0.5 source as of 2026-10-03
  • gemini-3.1-pro-preview usd_per_million_input_tokens $4 source as of 2026-10-03
  • gemini-3.1-pro-preview usd_per_million_input_tokens $2 source as of 2026-10-03
  • gemini-3.5-flash-lite (Batch) usd_per_million_input_tokens $0.15 source as of 2026-10-21
  • gemini-3.5-flash-lite usd_per_million_input_tokens $0.3 source as of 2026-10-21
  • gemini-3.8-flash usd_per_million_input_tokens $1.5 source as of 2026-10-03
  • gemini-3.8-flash usd_per_million_input_tokens $0.75 source as of 2026-10-03
  • gemini-2.5-pro usd_per_million_output_tokens $15 source as of 2026-10-03
  • gemini-2.5-pro usd_per_million_output_tokens $10 source as of 2026-10-03
  • gemini-3-flash-preview usd_per_million_output_tokens $3 source as of 2026-10-03
  • gemini-3.1-pro-preview usd_per_million_output_tokens $18 source as of 2026-10-03
  • gemini-3.1-pro-preview usd_per_million_output_tokens $12 source as of 2026-10-03
  • gemini-3.5-flash-lite (Batch) usd_per_million_output_tokens $1.25 source as of 2026-10-21
  • gemini-3.5-flash-lite usd_per_million_output_tokens $2.5 source as of 2026-10-21
  • gemini-3.8-flash usd_per_million_output_tokens $7.5 source as of 2026-10-03
  • gemini-3.8-flash usd_per_million_output_tokens $3.75 source as of 2026-10-03

2 of 7 candidates have no sourced price row. 2 are excluded from the Input $/1M comparison (chat_completion_input). min sourced input SKU among candidates; not quality-adjusted

Sourced Input $/1M for Long-context agent workflows

Candidates for this job that already have a matching sourced price (chat_completion_input), cheapest first. As of 2026-10-27. Each figure is one SKU in that unit, not a blend of input and output, not a cross-modal rate, and not a Depot score. min sourced input SKU among candidates; not quality-adjusted The Depot Pick is not a price winner.

ProviderInput $/1MSKUSourceAs of
Anthropic$0.1Haiku 5.5 (prompts up to 100K tokens)pricing page2026-10-27
OpenAI$0.1gpt-6-lunapricing page2026-10-03
MiniMax$0.15MiniMax-M3.1-Flashpricing page2026-10-19
Google AI (Gemini)Depot Pick$0.3gemini-3.5-flash-litepricing page2026-10-21
Moonshot (Kimi)$0.95kimi-k2.6pricing page2026-10-20

Runners-up

When NOT to use the top pick

Quality on tool use matters more than raw context — then Anthropic; enterprise GCP already → Vertex; want Kimi K3 1M ctx at $3/$15 — moonshot.

Full ranking (score table)

Ranked with this JTBD's weights. Scores are curated estimates unless marked otherwise. Agents: prefer /api/decide then this table for tie-breaks.

#ProviderOverallFit$/perfLatencyDXAs of
1Google AI (Gemini)Depot Pick8.49.28.88.58.22026-10-19
2Anthropic8.49.76.88.79.22026-10-19
3OpenAI8.29.66.28.89.52026-09-14
4Moonshot (Kimi)7.98.68.47.88.22026-09-14
5Alibaba Cloud Model Studio (DashScope)7.98.68.28.47.22026-09-14
6MiniMax7.88.48.87.88.22026-09-14
7AI21 Labs7.57.67.28.27.22026-09-14

Score methodology for this job

Capability includes context window + needle quality. Gemini often wins value at 1M; Claude strong at high quality long context.

  • capabilityFit: 25%
  • pricePerformance: 18%
  • latencyReliability: 15%
  • developerDx: 15%
  • privacyPosture: 10%
  • lockInRisk: 7%
  • maturity: 10%