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GPT-5.1-Codex-Max vs Qwen3.8 Flash

Qwen3.8 Flash costs 21.3x less per output token than GPT-5.1-Codex-Max ($0.47 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.

GPT-5.1-Codex-MaxOpenAI
Coding
No data
Overall
No data
Output $/M
$10.00
Qwen3.8 FlashQwen
Coding
No data
Overall
No data
Output $/M
$0.47
vs $10.00 vs $0.47 per 1M output tokens. Qwen3.8 Flash is 21.3x cheaper.

GPT-5.1-Codex-Max or Qwen3.8 Flash: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. Compare them on price and context instead.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-5.1-Codex-Max not benchmarked, Qwen3.8 Flash not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
GPT-5.1-Codex-Max $10.00 against Qwen3.8 Flash $0.47 per million output tokens, 21.3x apart.
Context window
GPT-5.1-Codex-Max takes 400K tokens, Qwen3.8 Flash 1M.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards measured
Coding benchmarks for GPT-5.1-Codex-Max and Qwen3.8 Flash
BenchmarkGPT-5.1-Codex-MaxQwen3.8 FlashLeader
LiveBench codingNot measured87.4%Only Qwen3.8 Flash measured
LiveCodeBench (Vals AI)83.6%Not measuredOnly GPT-5.1-Codex-Max measured
Vibe Code Bench (Vals AI)22.2%Not measuredOnly GPT-5.1-Codex-Max measured
Terminal-Bench60.4%Not measuredOnly GPT-5.1-Codex-Max measured
ALE-Bench1228.3Not measuredOnly GPT-5.1-Codex-Max measured
Neither model has a result on the other 34 boards here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
Agentic work benchmarks for GPT-5.1-Codex-Max and Qwen3.8 Flash
BenchmarkGPT-5.1-Codex-MaxQwen3.8 FlashLeader
LiveBench researchNot measured74.6%Only Qwen3.8 Flash measured
LiveBench browserNot measured48.0%Only Qwen3.8 Flash measured
LiveBench long agenticNot measured37.1%Only Qwen3.8 Flash measured
METR time horizons70.8%Not measuredOnly GPT-5.1-Codex-Max measured
Neither model has a result on the other 37 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Reasoning benchmarks for GPT-5.1-Codex-Max and Qwen3.8 Flash
BenchmarkGPT-5.1-Codex-MaxQwen3.8 FlashLeader
LiveBench reasoningNot measured87.4%Only Qwen3.8 Flash measured
Neither model has a result on the other 21 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Math benchmarks for GPT-5.1-Codex-Max and Qwen3.8 Flash
BenchmarkGPT-5.1-Codex-MaxQwen3.8 FlashLeader
ProofBench9.0%Not measuredOnly GPT-5.1-Codex-Max measured
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
Web design benchmarks for GPT-5.1-Codex-Max and Qwen3.8 Flash
BenchmarkGPT-5.1-Codex-MaxQwen3.8 FlashLeader
LiveBench designNot measured72.8%Only Qwen3.8 Flash measured
Neither model has a result on the other 8 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
General benchmarks for GPT-5.1-Codex-Max and Qwen3.8 Flash
BenchmarkGPT-5.1-Codex-MaxQwen3.8 FlashLeader
LiveBench data analysisNot measured74.2%Only Qwen3.8 Flash measured
Neither model has a result on the other 45 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5.1-Codex-Max$0.0063Fits in one request
Qwen3.8 Flash$0.0004Fits in one request

Qwen3.8 Flash costs less

Repository review

50K input + 3K output tokens

GPT-5.1-Codex-Max$0.092Fits in one request
Qwen3.8 Flash$0.0089Fits in one request

Qwen3.8 Flash costs less

Agent loop

200K input + 10K output tokens

GPT-5.1-Codex-Max$0.350Fits in one request
Qwen3.8 Flash$0.035Fits in one request

Qwen3.8 Flash costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5.1-Codex-Max and Qwen3.8 Flash
SpecificationOpenAI GPT-5.1-Codex-MaxQwen3.8 Flash
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 4, 2025August 26, 2026
Context window400K1M
Input $/Mtok$1.25$0.15
Output $/Mtok$10.00$0.47
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.1-Codex-Max better than Qwen3.8 Flash for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GPT-5.1-Codex-Max or Qwen3.8 Flash?

GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.8 Flash costs $0.15 and $0.47. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-5.1-Codex-Max or Qwen3.8 Flash?

Qwen3.8 Flash, with 1M tokens against 400K.

Has any benchmark tested both GPT-5.1-Codex-Max and Qwen3.8 Flash?

No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. Compare them on price and context instead. Data as of October 21, 2026.

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