Same model, different accuracy: LLM inference provider endpoint accuracy
What the benchmark found. Fireworks AI and Telnyx tied for the task-success lead at 99.5%. The leading score represents approximately 597 exact successful results from 600 requests; operational failure rates were Fireworks AI 0.0%; Telnyx 0.0%. Measured across 10 LLM inference providers serving GLM 5.3 Flash, 600 requests each, Sep 3, 2026.
Providers measured. Baseten, DeepInfra, Fireworks AI, Modal, Nebius, Novita AI, Parasail, Telnyx, Together AI, and Z.AI, each serving GLM 5.3 Flash behind an OpenAI-compatible chat-completions endpoint.
What this page compares. Holding the model name constant does not guarantee identical usable output. Providers can differ in malformed responses, truncation, transport reliability, and whether the returned object exactly matches the requested task schema. This page sorts the run by task success rate.
How to read the result. Task success uses all 600 submitted requests per provider as its denominator. A response counts only when it is schema-valid and exactly correct under the task-family rubric; operational failures, malformed JSON, incomplete objects, and wrong answers all reduce the score.
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 with the model identifiers below; the links open the provider documentation used to verify each adapter.
- Fireworks AI:
accounts/fireworks/models/glm-5p3-flash· API docs ↗ - Telnyx:
zai-org/GLM-5.3-Flash· API docs ↗ - Modal:
zai-org/GLM-5.3-Flash· API docs ↗ - Together AI:
zai-org/GLM-5.3-Flash· API docs ↗ - Parasail:
zai-org/GLM-5.3-Flash· API docs ↗ - Z.AI:
glm-5.3-flash· API docs ↗ - Baseten:
zai-org/GLM-5.3-Flash· API docs ↗ - DeepInfra:
zai-org/GLM-5.3-Flash· API docs ↗ - Novita AI:
zai-org/glm-5.3-flash· API docs ↗ - Nebius:
zai-org/GLM-5.3-Flash· API docs ↗
Which LLM inference provider returned the most exactly correct answers for the same model?
Higher is better. Correct-request counts make the percentage concrete, while failure rate and end-to-end p99 separate answer quality from service reliability and speed.
| Provider / API | Task success | Exact successes | Failure rate | End-to-end p99 |
|---|---|---|---|---|
| Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗ | 99.5% | 597 / 600 | 0.0% | 10.5 s |
| Telnyxzai-org/GLM-5.3-Flash ↗ | 99.5% | 597 / 600 | 0.0% | 3.48 s |
| Modalzai-org/GLM-5.3-Flash ↗ | 99.3% | 596 / 600 | 0.0% | 3.28 s |
| Together AIzai-org/GLM-5.3-Flash ↗ | 98.5% | 591 / 600 | 1.0% | 12.4 s |
| Parasailzai-org/GLM-5.3-Flash ↗ | 98.3% | 590 / 600 | 1.3% | 11.5 s |
| Z.AIglm-5.3-flash ↗ | 97.5% | 585 / 600 | 0.2% | 8.59 s |
| Basetenzai-org/GLM-5.3-Flash ↗ | 95.8% | 575 / 600 | 0.0% | 3.23 s |
| DeepInfrazai-org/GLM-5.3-Flash ↗ | 95.5% | 573 / 600 | 4.0% | 17.1 s |
| Novita AIzai-org/glm-5.3-flash ↗ | 94.2% | 565 / 600 | 4.0% | 7.25 s |
| Nebiuszai-org/GLM-5.3-Flash ↗ | 84.8% | 509 / 600 | 14.8% | 15.0 s |
Provider by provider, in the order above
- Fireworks AI: 99.5% task success (597 of 600 exact), 0.0% failures, end-to-end p99 10.49 seconds.
- Telnyx: 99.5% task success (597 of 600 exact), 0.0% failures, end-to-end p99 3.48 seconds.
- Modal: 99.3% task success (596 of 600 exact), 0.0% failures, end-to-end p99 3.28 seconds.
- Together AI: 98.5% task success (591 of 600 exact), 1.0% failures, end-to-end p99 12.45 seconds.
- Parasail: 98.3% task success (590 of 600 exact), 1.3% failures, end-to-end p99 11.51 seconds.
- Z.AI: 97.5% task success (585 of 600 exact), 0.2% failures, end-to-end p99 8.59 seconds.
- Baseten: 95.8% task success (575 of 600 exact), 0.0% failures, end-to-end p99 3.23 seconds.
- DeepInfra: 95.5% task success (573 of 600 exact), 4.0% failures, end-to-end p99 17.07 seconds.
- Novita AI: 94.2% task success (565 of 600 exact), 4.0% failures, end-to-end p99 7.25 seconds.
- Nebius: 84.8% task success (509 of 600 exact), 14.8% failures, end-to-end p99 14.97 seconds.
Where should exact task success lead the provider choice?
- Contract-field writeback. Require task success first because a fast incomplete object can write incorrect dates, renewal terms, or notice periods into another system.
- Automated ticket triage. Use exact category-and-priority success when the answer routes work without a human reviewing each result.
- Meeting-note lookup. Use task success when the answer is consumed as a fact; latency becomes the tie-breaker among providers that clear the required quality bar.
How is LLM inference provider latency and accuracy measured here?
- Same model. Every endpoint serves GLM 5.3 Flash; the provider layer is the only variable.
- Same request. One-turn, non-tool chat completion in JSON-object mode, streaming on, temperature 0, top-p 1, reasoning low, 256-token ceiling, 20-second timeout, concurrency one.
- Client clock, pinned tokenizer. Time to first token, end-to-end latency and tokens per second come from the client; visible answer tokens are counted with a pinned
tiktoken o200k_basetokenizer, never provider-reported usage. - Exact task success. An answer counts only when it is schema-valid and exactly correct under the task rubric. Timeouts, HTTP errors and malformed responses stay in the denominator.
- Serverless endpoints only. Dedicated capacity is outside the comparison. Runner and per-request records: openbenchmarks-labs/inference ↗.
Inference provider accuracy: benchmark-specific questions
Which provider completed the most exact-correct microtasks?
Fireworks AI and Telnyx tied for the task-success lead at 99.5%. The leading score represents approximately 597 exact successful results from 600 requests; operational failure rates were Fireworks AI 0.0%; Telnyx 0.0%.
What makes a contract extraction count as accurate here?
Contract extraction counts only when every required field is present and typed correctly; strings and enums compare exactly, dates use ISO YYYY-MM-DD, and the full object must match. Correct-field diagnostics do not create partial task-success credit.
How can two providers serving GLM 5.3 Flash get different success rates?
Even with the same requested model, delivery layers can differ in truncation, malformed output, model routing, stream completion, and operational reliability. Task success measures the usable result produced by the complete provider path, not model intelligence in isolation.
Does quantization explain the accuracy differences between providers?
The benchmark does not read provider-side serving configuration, so it cannot attribute a difference to quantization, batching or routing. It records the observable result: the same requested model, the same prompts, and a different share of exactly correct answers per endpoint. Where a provider documents its serving precision, the linked API documentation is the place to check.
Same run, other questions
- LLM inference provider benchmark: complete scorecard and methodology →
- Most reliable LLM API provider: failure rate, errors, timeouts and usable answers →
- Most reliable LLM API for structured output and JSON schema extraction →
- Best LLM inference provider for RAG answer extraction →
- Best LLM inference provider: speed, accuracy and failures compared →
- Fireworks AI vs Together AI →
- Fastest LLM inference provider: time to first token, tokens per second and end-to-end latency →
Read the complete LLM inference provider benchmark
Metric definitions, request scheduling, task rubrics, failure handling, and the complete provider scorecard live on the LLM Inference Provider Benchmark →