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Benchmark

Humanity's Last Exam

A multi-domain benchmark of about 2,500 graduate-level questions across subjects like chemistry, economics and literature, testing AI reasoning limits.

Known aliases

  • HLE
  • HLE w/ Tools
  • Humanity's Last Exam

Current stories

buildOne report1 publisher

DeepSeek V4.1 Flash's 36x discount to Claude Opus 5 depends on off-peak hours and low effort

DeepSeek V4.1 Flash costs $2.10 off-peak for an agent run that costs $75 on Claude Opus 5, about 36x less, according to a dev.to analysis. Paying peak-hour rates halves that gap to about 18x, and running at max effort cuts it to about 15x.

Publishers:dev.to

Reality

Evidence35
Adoption
Not scored
Hype gap+35
Incentives
Not scored
Confidence35
buildOne report1 publisher

Chain-of-thought monitors stop flagging errors when told a wrong answer is correct

Seven chain-of-thought monitors in a LessWrong study flagged 66 points fewer flawed physics solutions when their 'known correct' answer was wrong. Any pipeline that hands its monitor a reference answer depends on that reference being right.

Publishers:lesswrong.com

Reality

Evidence62
Adoption
Not scored
Hype gap+5
Incentives
Not scored
Confidence58
buildConfirmed4 publishers

DeepSeek open-sources the harness, then raises the price of the model

Harness v0.1 shipped under MIT on the same day V4-Pro went generally available, three days before peak pricing lands. The lock-in it targets is the runtime, not the weights.

Perspective Coverage

4 publishers
Builder
Builder 51%
Operator
Operator 31%
Investor
Investor 18%

Reality

Evidence55
Adoption
Not scored
Hype gap+25
Incentives70
Confidence58
buildOne report1 publisher

DeepSeek reroutes every V4-Pro API request to V4.1-Flash from 14 September

Flash's off-peak input price is under a quarter of what V4-Pro cost, and on DeepSeek's own table it beats the old Pro checkpoint on Terminal-Bench, but it scores 36.8 on Humanity's Last Exam and no V4.1-Pro has a date.

Publishers:dev.to

Reality

Evidence42
Adoption52
Hype gap+30
Incentives72
Confidence40
productOne report1 publisher

Fireworks' own DeepSWE numbers put four coding models inside the noise band

The vendor selling the cheapest model in the comparison reports a 0.7-point quality spread across four frontier models against run-to-run variation of 1.4 to 3.2 points. That leaves price per task, $0.43 against an implied $6.45 for GPT-6 Astra.

Publishers:fireworks.ai

Reality

Evidence42
Adoption18
Hype gap+28
Incentives88
Confidence58
buildOne report1 publisher

Claude Sonnet 4.5 follows a constraint on its own reasoning trace 2.7% of the time

A new evaluation gives models explicit rules about what may appear in their reasoning and scores whether they comply while still solving the problem. Compliance rose with model size and fell with more RL training.

Publishers:arxiv.org

Reality

Evidence58
Adoption
Not scored
Hype gap−5
Incentives35
Confidence60
investOne report1 publisher

DeepSeek's smallest model beats its own 28-day-old flagship on seven of eight shared scores

The V4.1-Flash change log puts Terminal Bench 2.1 at 90.6 against V4-Pro's 87.9 and DeepSWE at 74.2 against 62.7. DeepSeek says API prices came down with the release and points to a pricing page for the amounts.

Publishers:api-docs.deepseek.com

Reality

Evidence45
Adoption30
Hype gap+30
Incentives80
Confidence55
buildOne report1 publisher

AllSpark ran each Iris benchmark twice to separate the model from its scaffolding

The 35-billion-parameter Iris-mini and the 397-billion-parameter Iris-pro build on Qwen models and run at 256,000 tokens of context. AllSpark also published the training recipe and scored every benchmark twice, once with context management switched off.

Publishers:the-decoder.com

Reality

Evidence35
Adoption12
Hype gap+30
Incentives70
Confidence55
buildOne report1 publisher

GLM-5.3-Flash buys seven retries for the price of one Kimi K3 call

Artificial Analysis scores GLM-5.3-Flash 42 at $0.25 a task and Kimi K3 44 at $2.00 a task. At eight-to-one on price, a two-point composite gap settles nothing, and the cost of an hour of human review decides it.

Publishers:dev.to

Reality

Evidence56
Adoption
Not scored
Hype gap+12
Incentives30
Confidence55
buildOne report1 publisher

A 33-run sweep prices OpenAI's reasoning_effort ladder at 2.3x for identical answers

Eleven tasks with pre-computed answer keys, three runs each, seven effort settings. Everything from low upward scored 33 of 33, so the only thing the top rung buys is the number on the launch page.

Publishers:dev.to

Reality

Evidence62
Adoption45
Hype gap+15
Incentives55
Confidence58
leadershipConfirmed3 publishers

ARC Prize puts Astra 37 points below the score OpenAI led with

The same model produced 99.9% in OpenAI's launch post and 62.7% on the benchmark authors' neutral harness, and Astra's input tokens cost double GPT-5.6 Sol's, which leaves the vendor table doing very little work in a purchase decision.

Perspective Coverage

3 publishers
Builder
Builder 27%
Operator
Operator 37%
Investor
Investor 36%

Reality

Evidence66
Adoption32
Hype gap+61
Incentives79
Confidence71
leadershipOne report1 publisher

GLM-5.3-Flash benchmarks its tenth-of-the-price claim against its own predecessor

The MIT-licensed 320B model card claims it beats GLM-5.2 at a tenth of the price and approaches Claude Opus 4.8 on coding, but it names no dollar rate, and the comparisons are largely the vendor's own.

Publishers:huggingface.co

Reality

Evidence34
Adoption18
Hype gap+46
Incentives82
Confidence61
investOne report1 publisher

Astra's 99.9% holds up only on the harness OpenAI ran itself

ARC Prize put GPT-6 Astra at 62.7% against OpenAI's 99.9%, and the third-party composite index has it 0.3 points above the model it replaces, which leaves the 20% safety compute overhead as the clearest number in the launch.

Publishers:techtimes.com

Reality

Evidence57
Adoption42
Hype gap+58
Incentives74
Confidence54
scienceOne report1 publisher

Token efficiency absorbs GPT-6 Astra's 2.5x price increase inside the coding harness

Artificial Analysis priced the new model at $10 and $50 per million tokens, and the threefold token cut it measured in the Codex agent harness covers that increase where the roughly 10% cut on its intelligence suite does not.

Reality

Evidence63
Adoption
Not scored
Hype gap+16
Incentives62
Confidence57
buildOne report1 publisher

Dated AI forecasts have no scoreboard, and building against one costs weeks you can count

A developer graded 18 expired AI predictions against their own words. The best mark he shows is a C+, and the one he built on took six weeks out of his own roadmap.

Publishers:dev.to

Reality

Evidence30
Adoption38
Hype gap+22
Incentives62
Confidence32
buildOne report1 publisher

A harness gain is not a leaderboard win: reading the J-Space DeepSeek report properly

A viral X post said an inference-time text layer put DeepSeek V4 Pro ahead of Fable 5 on every task. The report it points to shows single runs, nine benchmarks, and two losses.

Publishers:runtimewire.com

Reality

Evidence40
Adoption18
Hype gap+55
Incentives62
Confidence45
buildConfirmed3 publishers

OpenAI puts latency on the price list: 750 tokens/sec, gated by workload fit

The Ultrafast preview runs GPT-5.6 Sol on Cerebras hardware for a hand-picked customer list. That makes capacity allocation, not model choice, the constraint your architecture has to survive.

Perspective Coverage

3 publishers
Builder
Builder 42%
Operator
Operator 33%
Investor
Investor 25%

Reality

Evidence42
Adoption24
Hype gap+32
Incentives78
Confidence58