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Model comparison

GPT-5.2-Codex vs Ling 2.6 Flash

Data verified

Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

59.1/100
Margin
15.2pts
← winning
InclusionAI
43.87/100
0 category wins0 category wins

Public leaderboard positions: GPT-5.2-Codex #58 (Supported); Ling 2.6 Flash #154 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GPT-5.2-Codex and Ling 2.6 Flash share 11 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to GPT-5.2-Codex; 7 to Ling 2.6 Flash.

Updated July 21, 2026
Shared results
11
GPT-5.2-Codex only
4
Ling 2.6 Flash only
7
Comparable categories
0 / 8

Benchmark data for GPT-5.2-Codex and Ling 2.6 Flash is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

GPT-5.2-Codex has the larger context window at 400K, compared with 262K for Ling 2.6 Flash.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for GPT-5.2-Codex and Ling 2.6 Flash
CategoryGPT-5.2-CodexΔLing 2.6 Flash
CodingGPT-5.2-CodexNot measuredMarginNo overlapLing 2.6 Flash27.0
KnowledgeGPT-5.2-CodexNot measuredMarginNo overlapLing 2.6 Flash59.0
Inst. FollowingGPT-5.2-CodexNot measuredMarginNo overlapLing 2.6 Flash57.0

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricGPT-5.2-CodexLing 2.6 FlashComparison
Input / output priceUSD per 1M tokensGPT-5.2-Codex$1.75 input / $14 outputLing 2.6 FlashNot availableA complete price comparison is not available.
Generation speedtokens per secondGPT-5.2-Codex123 tok/sLing 2.6 Flash209.5 tok/sLing 2.6 Flash has the higher measured throughput.
First-answer latencyseconds to first tokenGPT-5.2-Codex87.34 sLing 2.6 Flash1.07 sLing 2.6 Flash reaches the first token sooner.
Context windowmaximum listed tokensGPT-5.2-Codex400KLing 2.6 Flash262KGPT-5.2-Codex lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkGPT-5.2-CodexLing 2.6 FlashResult
τ²-bench resultsSource 92.1%86%GPT-5.2-Codex leads
Gert LabsSource 51.79%Not comparable
JobBenchSource 26.0%Not comparable
GDPval-AASource 2.2%Not comparable
GDPval-AASource 545Not comparable
AA Agentic IndexSource 2.3%Not comparable
Coding
BenchmarkGPT-5.2-CodexLing 2.6 FlashResult
Vibe Code BenchSource 37.91%Not comparable
AA-SciCodeSource 54.6%27.1%GPT-5.2-Codex leads
SciCodeSource 27%Not comparable
AA Coding IndexSource 25.3%Not comparable
Reasoning
BenchmarkGPT-5.2-CodexLing 2.6 FlashResult
AA-LCRSource 75.7%25.0%GPT-5.2-Codex leads
CritPtSource 8.7%0.0%GPT-5.2-Codex leads
Knowledge
BenchmarkGPT-5.2-CodexLing 2.6 FlashResult
Artificial Analysis Intelligence IndexSource 40.1%14.1%GPT-5.2-Codex leads
AA-GPQA DiamondSource 89.9%59.3%GPT-5.2-Codex leads
AA-HLESource 33.5%6.2%GPT-5.2-Codex leads
AA-Omniscience IndexSource -2.5%-65.7%GPT-5.2-Codex leads
AA-Omniscience AccuracySource 40.7%15.4%GPT-5.2-Codex leads
AA-Omniscience Hallucination RateSource 72.8%95.8%GPT-5.2-Codex leads
GPQASource 59%Not comparable
Multimodal
BenchmarkGPT-5.2-CodexLing 2.6 FlashResult
AA-MMMU-ProSource 76.3%Not comparable
Inst. Following
BenchmarkGPT-5.2-CodexLing 2.6 FlashResult
AA-IFBenchSource 77.6%57.4%GPT-5.2-Codex leads
IFBenchSource 57%Not comparable
Frequently Asked Questions (3)

Can I compare GPT-5.2-Codex and Ling 2.6 Flash on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for GPT-5.2-Codex and Ling 2.6 Flash today?

GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens Ling 2.6 Flash: Pricing unavailable Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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Last updated: July 21, 2026

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