Best inference provider for companies undergoing AI transformation
What the benchmark found. Baseten led E2E P99 at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. The benchmark informs short operational workloads; enterprise selection still requires workload replay and non-performance diligence.
What this page compares. An AI transformation program rarely has one inference requirement. Early production workloads often mix document lookup, operational classification, and structured extraction, so this page uses the balanced pooled benchmark to frame provider selection across latency, exact output yield, streaming behavior, and service failures.
How to read the result. Treat the table as measured technical diligence, not a universal procurement score. Define separate acceptance thresholds for task success, failure rate, complete-response latency, and visible streaming; then validate security, data residency, support, capacity, governance, and commercial terms outside this benchmark.
Workload and configuration. Every provider received the same 600 deliberately easy structured tasks: 200 meeting-note lookups, 200 ticket-triage classifications, and 200 contract-term extractions. GLM 5.3 Flash ran with streaming enabled, reasoning low, temperature 0, top-p 1, a 256-token output ceiling, one attempt, a 20-second timeout, and per-provider concurrency one.
APIs used. The runner called each provider's OpenAI-compatible chat-completions surface. Baseten, Modal, Telnyx, Novita AI, Z.AI, Fireworks AI, Parasail, Together AI, Nebius, and DeepInfra used the model identifiers below; the links open the provider documentation used to verify each adapter.
- Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Novita AI:
zai-org/glm-5.3-flash· API docs ↗ - Z.AI:
glm-5.3-flash· API docs ↗ - Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - Nebius:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗
Inference providers across common transformation workloads
This focused table adds deltas from both the latency and accuracy leaders while retaining TTFA, generation P95, and failure rate. It differs from the full benchmark and deliberately avoids a synthetic enterprise score.
| Provider / API | E2E P99 | Δ vs fastest | TTFA P95 | Generation P95 | Task success | Δ vs most accurate | Failure rate |
|---|---|---|---|---|---|---|---|
| Basetenzai-org/GLM-5.3-Flash ↗ | 3.23 s | 0 ms | 1.16 s | 61.6 tok/s | 95.8% | -3.7 pp | 0.0% |
| Modalzai-org/GLM-5.3-Flash ↗ | 3.28 s | +50 ms | 1.09 s | 84.7 tok/s | 99.3% | -0.2 pp | 0.0% |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 3.48 s | +250 ms | 1.34 s | 67.5 tok/s | 99.5% | 0.0 pp | 0.0% |
| Novita AIzai-org/glm-5.3-flash ↗ | 7.25 s | +4020 ms | 3.41 s | 29.6 tok/s | 94.2% | -5.3 pp | 4.0% |
| Z.AIglm-5.3-flash ↗ | 8.59 s | +5362 ms | 3.14 s | 27.8 tok/s | 97.5% | -2.0 pp | 0.2% |
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 10.5 s | +7259 ms | 5.83 s | 27.9 tok/s | 99.5% | 0.0 pp | 0.0% |
| Parasailzai-org/GLM-5.3-Flash ↗ | 11.5 s | +8283 ms | 5.54 s | 22.8 tok/s | 98.3% | -1.2 pp | 1.3% |
| Together AIzai-org/GLM-5.3-Flash ↗ | 12.4 s | +9221 ms | 2.26 s | 34.0 tok/s | 98.5% | -1.0 pp | 1.0% |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 15.0 s | +11742 ms | 7.11 s | 78.8 tok/s | 84.8% | -14.7 pp | 14.8% |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 17.1 s | +13842 ms | 6.41 s | 9.0 tok/s | 95.5% | -4.0 pp | 4.0% |
Transformation workstreams represented in the dataset
- Knowledge access. Meeting-note lookup represents extracting one actionable fact from context already supplied to the model.
- Operations automation. Ticket triage represents bounded decisions that route work into existing business processes.
- Document digitization. Contract extraction represents converting unstructured business text into typed system-of-record fields.
Enterprise inference diligence for AI transformation
Can one benchmark name the best provider for an entire AI transformation?
No. A company-wide transformation spans workloads and requirements that cannot be reduced to one provider rank. Baseten led E2E P99 at 3.23 seconds; Telnyx and Fireworks AI led task success at 99.5%. The complete scorecard keeps latency, streaming speed, accuracy, and failures separate rather than blending them into one synthetic score. The benchmark informs short operational workloads; enterprise selection still requires workload replay and non-performance diligence.
Which measured gates should an enterprise pilot set before provider selection?
Set workload-specific floors for exact task success and operational failure rate, then latency budgets for blocking and interactive paths. Replay representative company data before moving from this shortlist to a production decision.
What procurement and governance questions remain outside these results?
The benchmark does not assess security controls, privacy terms, data residency, governance, support, reserved capacity, enterprise pricing, procurement risk, or performance on the company's private workloads.
Read the complete inference benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the Inference Benchmark →