Add GPT-5.6 Luna benchmark result

README.md

@@ -112,20 +112,22 @@ The model generates `clean_target` and terminates at
 `<|end_target_text|>`.
 
 The instruction-tuned prompt-only Qwen and Gemma references, their Fluid
-fine-tunes, and the DeepSeek V4 Flash comparison instead use chat messages.
-Qwen, Gemma, and the Fluid fine-tunes use each GGUF's native template;
-DeepSeek uses its official chat-completions API. All receive the same private
-system instruction and the row's `asr_text` verbatim as the user message. The
-private instruction is intentionally not reproduced in this public repository.
+fine-tunes, DeepSeek V4 Flash, and GPT-5.6 Luna instead use chat messages. Qwen,
+Gemma, and the Fluid fine-tunes use each GGUF's native template; DeepSeek uses
+its official chat-completions API; Luna uses the local Codex Responses API. All
+receive the same private system instruction and the row's `asr_text` verbatim as
+the user message. The private instruction is intentionally not reproduced in
+this public repository.
 
 ## Metrics
 
 The Fluid 2 Beta rows use SGLang raw-completion inference. Instruction-tuned
 Qwen/Gemma comparison rows use llama.cpp chat-completion inference with their
 native GGUF templates and reasoning disabled during generation. DeepSeek uses
-the official API with thinking disabled. All inference paths use
-temperature-zero decoding. The public scoreboard reports strict exact match and
-character error rate (CER), while the audit also tracks
+the official API with thinking disabled. Those paths use temperature-zero
+decoding. Luna uses the local Codex Responses API with reasoning effort set to
+none and proxy/model sampling defaults. The public scoreboard reports strict
+exact match and character error rate (CER), while the audit also tracks
 whitespace-normalized exact match, improvement over the raw-ASR copy baseline,
 empty/control-token failures, and context-bucket breakdowns. Strict metrics
 remain case-, punctuation-, and formatting-sensitive because those are core
@@ -141,7 +143,7 @@ text-quality scores. Throughput and inference-failure counts still cover the ful
 <!-- FLUID2_EVAL_RESULTS_START -->
 ## Published model results
 
-Fluid 2 Beta results use deterministic greedy SGLang raw-completion inference. The prompt-only Qwen and Gemma references and their Fluid fine-tunes use deterministic greedy llama.cpp chat-completion inference with each GGUF's native chat template and reasoning disabled at generation. DeepSeek V4 Flash uses its official chat-completions API with thinking disabled. All eight models receive the same 7,161-row text-only development set; every chat model receives the same fixed private system instruction and verbatim `asr_text` user message. EM and CER exclude both EOS-only rows and generation-capped rows; the exclusion counts are shown explicitly.
+Fluid 2 Beta results use deterministic greedy SGLang raw-completion inference. The prompt-only Qwen and Gemma references and their Fluid fine-tunes use deterministic greedy llama.cpp chat-completion inference with each GGUF's native chat template and reasoning disabled at generation. DeepSeek V4 Flash uses its official chat-completions API with thinking disabled. GPT-5.6 Luna uses the local Codex Responses API with reasoning effort set to none. All nine models receive the same 7,161-row text-only development set; every chat model receives the same fixed private system instruction and verbatim `asr_text` user message. EM and CER exclude both EOS-only rows and generation-capped rows; the exclusion counts are shown explicitly.
 
 | Model | Scored rows | EOS-only excluded | Capped excluded | EM | CER | Evaluated revision |
 |---|---:|---:|---:|---:|---:|---|
@@ -153,12 +155,15 @@ Fluid 2 Beta results use deterministic greedy SGLang raw-completion inference. T
 | [Gemma 4 E2B IT (prompt only)](https://huggingface.co/google/gemma-4-E2B-it) | 7,034 | 121 | 6 | 5.7293% | 42.3142% | `3e22461f65e8` |
 | [FluidIntelligence](https://huggingface.co/altic-dev/FluidIntelligence) | 7,023 | 121 | 17 | 5.8522% | 27.5391% | `0980ee1c2019` |
 | [DeepSeek V4 Flash (prompt only)](https://api-docs.deepseek.com/quick_start/pricing) | 7,033 | 121 | 7 | 6.3984% | 46.1081% | `a26a7955944d` |
+| [GPT-5.6 Luna (prompt only)](https://developers.openai.com/) | 6,984 | 121 | 57 | 6.2285% | 32.8253% | `gpt-5.6-luna` |
 | Raw `asr_text` copy baseline (0.8B cohort) | 7,010 | 121 | 30* | 3.9372% | 39.0639% | — |
 
 The llama.cpp comparison runs use `temperature=0`, `top_k=1`, `top_p=1`, `min_p=0`, a fixed request seed, a 4,096-token completion limit, no speculative decoding, and `--reasoning off --reasoning-budget 0`.
 
 The DeepSeek run uses `deepseek-v4-flash`, `temperature=0`, `top_p=1`, a 4,096-token completion limit, and the official `thinking: {type: disabled}` switch. Its evaluated revision is the API-returned system fingerprint.
 
+The GPT-5.6 Luna run uses the local Codex Responses API, model `gpt-5.6-luna`, proxy/model sampling defaults with no sampling override, `reasoning.effort=none`, a 4,096-token output limit, and a 64-request client-side in-flight ceiling. At the requested early stop, the 57 unreturned proxy-timeout rows are treated as capped exclusions; one of those rows is also EOS-only, so the exclusion columns are not disjoint for this run.
+
 \* The raw-ASR copy baseline uses the identical 7,010-row scoring cohort as the 0.8B model. Its capped-excluded count is inherited from the 0.8B generation outcomes; the copy baseline itself does not generate or hit a token cap.
 
 ## Paired prompt-only comparison