benchmarks/inference/same model different accuracy
endpoint accuracy · same model, different providers · measured Sep 3, 2026

Same model, different accuracy: LLM inference endpoint accuracy compared

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. Every row requested the same GLM 5.3 Flash model with the same prompts, and the share of exactly correct, schema-valid answers still differs by endpoint. This page isolates that finding: the provider layer is the only variable.

How to read the result. Read task success as the endpoint's usable yield. Failure rate is separate, so a delivery problem is not read as a formatting one. The benchmark does not read provider-side configuration, so it reports the difference without attributing it to quantization, batching or routing.

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.

[01] measured comparison

Which LLM inference endpoint returns the most exactly correct answers for the same model?

Higher is better. Exact-success counts out of 600 make the gap concrete; failure rate and end-to-end p99 stay beside it.

benchmarks/inference/same-model-different-accuracyreviewed run
LLM inference providers sorted by exact task success rate
Provider / APITask successExact successesFailure rateEnd-to-end p99
Fireworks AIaccounts/fireworks/models/glm-5p3-flash ↗99.5%597 / 6000.0%10.5 s
Telnyxzai-org/GLM-5.3-Flash ↗99.5%597 / 6000.0%3.48 s
Modalzai-org/GLM-5.3-Flash ↗99.3%596 / 6000.0%3.28 s
Together AIzai-org/GLM-5.3-Flash ↗98.5%591 / 6001.0%12.4 s
Parasailzai-org/GLM-5.3-Flash ↗98.3%590 / 6001.3%11.5 s
Z.AIglm-5.3-flash ↗97.5%585 / 6000.2%8.59 s
Basetenzai-org/GLM-5.3-Flash ↗95.8%575 / 6000.0%3.23 s
DeepInfrazai-org/GLM-5.3-Flash ↗95.5%573 / 6004.0%17.1 s
Novita AIzai-org/glm-5.3-flash ↗94.2%565 / 6004.0%7.25 s
Nebiuszai-org/GLM-5.3-Flash ↗84.8%509 / 60014.8%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.
[02] workflow fit

Where does endpoint accuracy change the provider decision?

  • Provider failover. Two endpoints serving the same model are not interchangeable if one returns fewer exact objects; check task success before treating them as redundant.
  • Model evaluation. An eval run through one provider measures that endpoint, not the model. Re-run through a second provider before concluding about the model.
  • Structured pipelines. A schema-valid but wrong field is an accuracy miss the application cannot detect; endpoint accuracy is the floor of the pipeline's correctness.
[03] how it was measured

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_base tokenizer, 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 ↗.
[04] faq

Same model, different accuracy: questions this page answers

Why does the same model give different accuracy on different providers?

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%.

Which provider returned the most exactly correct answers for GLM 5.3 Flash?

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.

Is the accuracy difference caused by quantization?

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.

Is the accuracy difference caused by quantization?

The benchmark cannot say. It does not read provider-side serving configuration, so it reports the observable result: same requested model, same prompts, 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.

[05] related comparisons
[06] source benchmark

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 →