What is the best model for this workload,
under these constraints, right now?
Lattice turns model evaluation from an occasional research project into continuous infrastructure — designed around real workloads, not synthetic leaderboards, in a world where models, providers, and prompts change every week.
Not the leaderboard the world uses.
The benchmark your workload needs.
Lattice is a private benchmarking platform. Every org — and every product inside every org — runs its own suites, on its own workloads, with its own graders and constraints. It's the cheapest, fastest, and most accurate way to know which models actually work for your systems.
for the whole world.
- Someone else's test set
- Someone else's constraints
- Weeks-old at best
- Can't be verified against your traffic
Benchmarks built around real work — not a generic test suite.
Design in minutes. Run across any model or provider. Version, fork, compose, tag, and reuse test suites across teams. A benchmark can be twenty carefully chosen examples or millions of automated trials.
- 01Prompt & response cases
- 02Multi-turn conversations
- 03Tool-using agent tasks
- 04Structured-output requirements
- 05Coding & execution tasks
- 06Retrieval & research workflows
- 07Image & multimodal
- 08Long-context tests
- 09Adversarial & edge cases
- 10Production traces (anonymized)
- 11Synthetic test-set generation
- 12Parameterized dynamic cases
Some things are numbers.
Some things are preferences.
Lattice supports both — without pretending a piece of writing deserves an arbitrary 8.3/10.
There isn't one best model.
There's a frontier.
Lattice measures the complete execution profile — quality, latency, tail latency, cost, throughput, reliability — and identifies which models dominate different portions of the trade space under your constraints.
Lattice benchmarks the benchmark.
And every model becomes a fingerprint.
A leaderboard reduces a model to a position. Lattice builds a multidimensional profile instead — and it watches the tests themselves for staleness, redundancy, contamination, and unstable graders.
- instruction adherence0.94
- tool use0.88
- reasoning · long ctx0.81
- visual taste0.46
- fast extraction0.91
- code · agentic0.79
- refusal calibration0.83
- creative style0.58
- ✓discriminates strong models
- ✓judge agreement 0.87
- !3 redundant cases detected
- !2 cases show grader instability
- ✗7 cases suspect memorization
- ✗capability gap: multi-modal reasoning
Model benchmarking at the speed of innovation.
Models arrive weekly. Providers change. Prices fall. Context windows expand. Lattice continuously rebuilds the evidence you need to make good decisions — as infrastructure, not a dashboard someone remembers to check.