Intelligence

The market moves.
We prove by how much.

Every number on this page is computed from the same live catalog and the same measured benchmark instruments the switching engine runs on. Nothing here is estimated.

What this is: a public monthly record of what AI model prices actually did, which providers moved them, and which model is still the cheapest one good enough for a given job. Free to read, free to cite.

Computed 2026-10-04 05:28 UTC

477

Models tracked

Active entries in the live catalog.

76

Providers tracked

Distinct real providers with at least one live price. Aggregator listings are excluded.

1,724

Price moves in October 2026

748 up · 976 down. 29 new listings are counted separately.

Number of the month

+9900%

DeepSeek V4.1 Flash, same weights

$0.0030 at Relace against $0.300 at Venice, per MTok in.

Direction

1724 recorded price moves in October 2026: 976 cuts and 748 rises, so the market moved down on balance. 57% of all moves were cuts.

Four terms this page uses

MTok
One million tokens. Every price on this page is US dollars per million tokens, so models with different token accounting stay comparable.
Price move
One observed change to a live listed price, recorded in the append-only ledger with its direction. A model appearing for the first time is a new listing, never a move. Full definition.
Provider spread
The gap between the cheapest and dearest host serving identical weights. It is a routing choice, not a quality difference.
Equivalence band
The score range around your current model, widened by the benchmark's own measurement margin, that a candidate must sit inside before a switch is offered.

Price moves

What changed in October 2026

Recorded from the append-only price ledger: every observed move, per model and per host, on both the input and output side.

1724price movesOctober 2026
  • Decreases976
  • Increases748
  • New listings29
  • Total moves counts increases plus decreases only. New listings are shown here for context and are never folded into that total.
1,724

Total moves

Increases plus decreases. New listings are not folded in.

748

Increases

976

Decreases

1

New models

First seen in October 2026.

Top 5 increases

  • deepseek/deepseek-v4-flash

    OpenRouter

    +1450.5%

    blended

    $0.028 → $0.022 in (-20.0%) · $0.056 → $1.28 out (+2185.7%)

  • deepseek/deepseek-v4-flash

    OpenInference

    +1405.5%

    blended

    $0.0002 → $0.0050 in (+2425.3%) · $0.083 → $1.25 out (+1403.1%)

  • deepseek/deepseek-v4-pro-0813

    Baidu

    +900.0%

    blended

    $0.132 → $1.32 in (+900.0%) · $0.396 → $3.96 out (+900.0%)

  • z-ai/glm-5.2

    Baidu

    +890.1%

    blended

    $0.141 → $1.40 in (+890.1%) · $0.444 → $4.40 out (+890.1%)

  • deepseek/deepseek-v4-pro-0813

    Baidu

    +890.1%

    blended

    $0.133 → $1.32 in (+890.1%) · $0.400 → $3.96 out (+890.1%)

Top 5 decreases

  • ~deepseek/deepseek-v4-flash-latest

    OpenRouter

    -89.5%

    blended

    $0.0045 → $0.0045 in (-1.0%) · $1.28 → $0.131 out (-89.8%)

  • ~deepseek/deepseek-v4-flash-latest

    OpenRouter

    -89.4%

    blended

    $0.0051 → $0.0050 in (-1.0%) · $1.28 → $0.131 out (-89.8%)

  • ~deepseek/deepseek-v4-flash-latest

    OpenRouter

    -89.4%

    blended

    $0.0058 → $0.0057 in (-1.0%) · $1.28 → $0.131 out (-89.8%)

  • ~deepseek/deepseek-v4-flash-latest

    OpenRouter

    -89.3%

    blended

    $0.0066 → $0.0065 in (-1.0%) · $1.28 → $0.131 out (-89.8%)

  • ~deepseek/deepseek-v4-flash-latest

    OpenRouter

    -89.3%

    blended

    $0.0074 → $0.0073 in (-1.0%) · $1.28 → $0.131 out (-89.8%)

Who reprices most

Ranked over everything we hold. The leaderboard updates as more price moves are recorded.

  • 1OpenRouter745moves · 31 models
  • 2Relace279moves · 9 models
  • 3OpenInference142moves · 4 models
  • 4Morph121moves · 6 models
  • 5InferenceNet108moves · 7 models
  • 6Wafer102moves · 8 models
  • 7Baidu49moves · 8 models
  • 8Ionstream30moves · 3 models

Market structure

The same weights cost wildly different money

Identical model, different real provider. Aggregator listings are excluded, so every gap below is a genuine provider-to-provider spread you could act on today.

157

Models on 2+ providers

1

Median providers per model

33

Most providers on one model

254
162%
86
2–321%
49
4–912%
22
10+5%

Providers per model, across 411 models with at least one real (non-aggregator) endpoint. Most weights are single-sourced; a small tail is served everywhere, and that tail is where the spread lives.

  • DeepSeek V4.1 Flash

    deepseek/deepseek-v4.1-flash

    $0.0030$0.300

    Relace → Venice · 28 providers · per MTok in

    +9900%

    spread

  • Qwen3.8 27B

    qwen/qwen3.8-27b

    $0.024$0.990

    Wafer → Cerebras · 17 providers · per MTok in

    +4025%

    spread

  • DeepSeek V4 Flash 0731

    deepseek/deepseek-v4-flash-0731

    $0.015$0.440

    Relace → AtlasCloud · 26 providers · per MTok in

    +2795%

    spread

  • GLM 5.3

    z-ai/glm-5.3

    $0.050$1.40

    InferenceNet → Crusoe · 32 providers · per MTok in

    +2700%

    spread

  • GLM 5.2

    z-ai/glm-5.2

    $0.064$1.40

    Relace → Z.AI · 27 providers · per MTok in

    +2087%

    spread

  • DeepSeek V4 Flash 0423

    deepseek/deepseek-v4-flash

    $0.022$0.440

    Relace → Cloudflare · 16 providers · per MTok in

    +1864%

    spread

  • DeepSeek V3.2

    deepseek/deepseek-v3.2

    $0.209$3.00

    GMICloud → Mara · 13 providers · per MTok in

    +1337%

    spread

  • DeepSeek V4 Pro 0813

    deepseek/deepseek-v4-pro-0813

    $0.136$1.65

    Relace → Venice · 20 providers · per MTok in

    +1113%

    spread

Quality per dollar

The cheapest model that still clears the band

For each task class we take the leading published score, subtract that evaluation's measured margin, and pick the cheapest model still above the line. Only suites with a real measured margin appear, because a benchmark without one cannot back a claim.

  • gpqa

    GLM 5.3 Flash

    z-ai/glm-5.3-flash

    Scores 91.20 on aa:gpqa; bar 91.10 (leader 96.10 − margin ±5.00). 31 models clear it. Cheapest listing at Relace.

    cheapest qualifying 91.20bar 91.10leader 96.10band width ±5.00 (measured margin)
    $0.035

    per MTok in

  • hle

    Claude Opus 5.5

    anthropic/claude-opus-5.5

    Scores 61.40 on aa:hle; bar 59.73 (leader 61.40 − margin ±1.67). 1 model clear it. Cheapest listing at Azure.

    cheapest qualifying 61.40bar 59.73leader 61.40band width ±1.67 (measured margin)
    $4.00

    per MTok in

  • lcr

    DeepSeek V4.1 Flash

    deepseek/deepseek-v4.1-flash

    Scores 84.00 on aa:lcr; bar 83.67 (leader 88.67 − margin ±5.00). 9 models clear it. Cheapest listing at Relace.

    cheapest qualifying 84.00bar 83.67leader 88.67band width ±5.00 (measured margin)
    $0.0030

    per MTok in

  • scicode

    Claude Opus 5.5

    anthropic/claude-opus-5.5

    Scores 66.90 on aa:scicode; bar 61.90 (leader 66.90 − margin ±5.00). 2 models clear it. Cheapest listing at Azure.

    cheapest qualifying 66.90bar 61.90leader 66.90band width ±5.00 (measured margin)
    $4.00

    per MTok in

  • tau_banking

    Qwen3.8 27B

    qwen/qwen3.8-27b

    Scores 48.04 on aa:tau_banking; bar 45.72 (leader 50.72 − margin ±5.00). 9 models clear it. Cheapest listing at Wafer.

    cheapest qualifying 48.04bar 45.72leader 50.72band width ±5.00 (measured margin)
    $0.024

    per MTok in

  • terminalbench_v2_1

    Gemini 3.8 Flash

    google/gemini-3.8-flash

    Scores 87.64 on aa:terminalbench_v2_1; bar 86.39 (leader 91.39 − margin ±5.00). 8 models clear it. Cheapest listing at Google AI Studio.

    cheapest qualifying 87.64bar 86.39leader 91.39band width ±5.00 (measured margin)
    $0.375

    per MTok in

Benchmark saturation

An evaluation stops being usable when the spread between models collapses into the measurement margin. We require the observed spread to exceed twice the margin; a ratio at or below 1.0 means the instrument can no longer tell models apart.

  • tau_banking · aa:tau_banking

    spread 49.69 vs margin ±8.42 across 88 scored models, discriminating.

    2.95×vs 1.0× floor
  • terminalbench_v2_1 · aa:terminalbench_v2_1

    spread 88.02 vs margin ±10.33 across 88 scored models, discriminating.

    4.26×vs 1.0× floor
  • lcr · aa:lcr

    spread 86.67 vs margin ±9.45 across 151 scored models, discriminating.

    4.59×vs 1.0× floor
  • scicode · aa:scicode

    spread 49.50 vs margin ±5.33 across 165 scored models, discriminating.

    4.65×vs 1.0× floor
  • gpqa · aa:gpqa

    spread 61.20 vs margin ±5.84 across 154 scored models, discriminating.

    5.24×vs 1.0× floor
  • hle · aa:hle

    spread 59.60 vs margin ±1.67 across 165 scored models, discriminating.

    17.88×vs 1.0× floor

Newsletter

These numbers, weekly.

One email a week: the price moves we recorded, which switches cleared quality, and what changed for the cheapest model good enough for a given job. No pitch, unsubscribe in one click.

Method

How a switch gets measured

  • Live price sync

    Per-host prices refresh continuously from public provider feeds. A recommendation is priced against the catalog as it stands the moment you see it, not a quarterly snapshot.

  • Independent benchmark scores

    Quality comes from published third-party evaluations, per task class. We do not run our own private eval and we are never paid for placement.

  • The equivalence band

    A candidate model only qualifies when its score sits inside the band around your current model for the task class in question. Cheaper-but-worse never clears.

  • Measurement margin

    Every score carries its own uncertainty. We compute the real margin and require the gap to survive it before a switch is offered.

  • Latency ceilings

    Median latency is part of the decision, not an afterthought. Set a ceiling and candidates that breach it are dropped before cost is even compared.

  • Refusals with reasons

    When nothing clears, you get a stated reason, not a weaker suggestion. A quiet downgrade would cost you more than the saving is worth.

Cite and reuse

Take the numbers with you

Every figure here is free to quote, repost or chart, with attribution to CostMyAI. We would rather be the cited source than the hidden one.

Citation

CostMyAI Intelligence, September 2026. Retrieved 4 Oct 2026. https://www.costmyai.com/intelligence/2026-09

Permanent link

https://www.costmyai.com/intelligence/2026-09?ref=share&card=cite

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Archive

Every closed month, frozen and permanently linkable

At 00:00 UTC on the first of each month we write that month's final figures once and never touch them again. A correction is filed as a new restatement row that points back at the original, so the number you cited stays exactly as you cited it.