What both can do
Both Claude Opus 4.7 and Gemini ship roughly 1M tokens of context. Both support prompt caching at meaningfully reduced rates. Both can hold a stack of legal contracts, a year of customer history, or most production codebases in a single prompt.
The benchmarks (needle-in-haystack, long-document QA) put them within a few points of each other. On those benchmarks the choice looks like a coin flip.
Production isn't those benchmarks.
Where Claude wins on long-context
- Cross-passage reasoning. Tasks where the answer requires the model to combine information from passages 200K tokens apart. Claude reliably picks up the connections; Gemini occasionally treats the passages as independent.
- Code-shaped corpora. Whole-codebase analysis, refactor planning, "find the bug across these 80 files." Claude's code reasoning at long context is the strongest we've measured.
- Tool-use inside long-context sessions. When the agent needs to call tools partway through a session that's already 400K tokens deep, Claude's tool-call quality holds up better than Gemini's.
- Output structure on long inputs. Asking for a structured JSON summary of a 600K-token document — Claude returns valid, well-shaped JSON more reliably.
Where Gemini wins on long-context
- Video-shaped inputs. Multimodal long-context with video frames as inputs is Gemini's strongest suit. Claude handles images well but Gemini's video pipeline is materially better.
- Google Workspace integration. If the corpus is "everything in this Google Drive folder," Gemini's native integration shortcuts the data-pipe layer. Claude requires more wiring.
- Cost per cached token at the largest tier. Gemini's caching pricing in the 1M+ range is competitive in some configurations and worth testing.
- Latency on cached very-long inputs. For stable cached prefixes at the long end, Gemini's first-token latency is noticeably faster in our measurements.
The gotcha that flips the answer
What lives in the prompt matters more than the model picks. Three concrete cases:
- If the corpus is mostly code: Claude. Not close.
- If the corpus is mostly video frames or Google-native documents: Gemini. Not close.
- If the corpus is mixed prose and structured data: usually Claude, but with caveats — Gemini holds up well on tabular data, and the answer can flip based on the workload.
Most teams pick a model first and try to fit the workload to it. The workload should pick the model. Run a 1-day shadow eval on both, against your actual corpus and your actual questions, before committing.
The routing decisions we make weekly
Across our cohort of clients running long-context workloads, the routing usually lands:
- Codebase analysis, refactor agents, code-review tools: Claude. ~85% of our long-context code work runs here.
- Legal/contract analysis: Claude. Cross-document reasoning matters and Claude's structured output holds up.
- Customer-history briefs from Google Workspace: Gemini. The integration alone usually decides it.
- Video-input analysis (transcribed meetings, recorded sessions): Gemini.
- Multi-document Q&A on prose corpora: split. We default to Claude and shadow-test Gemini quarterly to make sure we haven't drifted past a flip point.
The multi-vendor argument
Procurement teams increasingly want multi-vendor AI infrastructure for negotiating leverage and outage resilience. Long-context is one of the workloads where keeping both providers warm makes the most sense — the workloads diverge enough that it's not arbitrary, and pricing changes on either side can flip the per-call math meaningfully.
The cost of multi-vendor: you maintain two prompt versions, two eval baselines, and two sets of cache breakpoints. About 20-30% extra prompt-engineering work per workload. The benefit: leverage when the next round of pricing comes around, and resilience when one provider has a bad week.
The cost discipline doesn't change
Whichever provider you pick, the same cost discipline applies (see prompt caching deep-dive): cache the static prefix, curate before you stuff, cap output, measure hit rate. The provider is a smaller cost lever than the caching strategy. Most "Claude is more expensive than Gemini" or vice versa complaints we see come down to one provider's cache being correctly configured and the other not.
The summary
Both ship 1M context. Both work. The corpus picks the provider — code → Claude, Workspace and video → Gemini, mixed → test and pick. Multi-vendor is increasingly worth the prompt-engineering tax for procurement leverage. The benchmark numbers are close enough to be misleading; run shadow evals on your own data before committing.
For the broader Claude long-context piece, see Opus 4.7 1M context. For Sonnet/Haiku routing inside the Claude family, see the routing piece.